Kimmo Pulakka Soft-Computing Based Control Schemes for QoS in Communication Networks

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Abstract The development of computers and new network-oriented applications has created a need for multi-service networks. These networks should be capable of transferring data of different applications according to their various requirements. The research and development work for these networks has been intensive from the late 1980's. Several different approaches have been created. For instance, ATM (Asynchronous Transfer Mode) networks, RSVP (Resource Reservation Protocol) and the DS (Differentiated Services) model have been developed for appropriate sharing of network resources between different network-oriented applications. In addition to quality of service (QoS) issues, pricing of transmission services is also an important design issue for both network operators and end-users. Pricing mechanisms of network operators should find a suitable balance between QoS and prices of the services based on characteristics of market environments and aims of operators. Intensive research work is continuing on both QoS and pricing mechanisms.

The research work of the present thesis consisted of two research subjects. In the first part of the research work, rate control mechanisms for assured services in the B-ISDN (Broadband - Integrated Service Data Networks) type of networks were studied. The purpose of the developed mechanisms was to maximize goodput and utilization of networks. In the second part of the work, control mechanisms for adjusting the relation of price and QoS of transmission services in DS-enabled IP (Internet Protocol) networks were studied. These mechanisms calculated the optimal relation of QoS and prices for fulfilling aims of the operators.

Lack of exact information for the control decisions was the common challenge in both research subjects. In the first subject, control decisions should be made using delayed load information of network nodes. In the second subject, controllers had not information about QoS and prices of services in network domains of other operators. In both subjects, advance information about behaviour and data transmission needs of end-users was also missing. The controllers developed were based on the use of various soft-computing algorithms. The soft-computing schemes used for the controllers varied from simple manually tuned fuzzy controllers to automatically tuned neuro-fuzzy systems. The research results of other studies have indicated that these algorithms are well-suited for control tasks where exact information for control has been missing. Simulators were used for testing the performance of the control systems.

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Contents

ABSTRACT ......................................................................................................................2 CONTENTS ......................................................................................................................3 LIST OF PUBLICATIONS .............................................................................................5 LIST OF MAIN SYMBOLS AND ACRONYMS ..........................................................7 LIST OF FIGURES........................................................................................................10 1. INTRODUCTION ......................................................................................................12 2. MULTI-SERVICE NETWORK ENVIRONMENTS..............................................14

2.1. INTERESTS OF DIFFERENT COMMUNICATION PARTIES ............................................14 2.2. PRINCIPAL ISSUES IN OFFERING PROPER SERVICES TO CUSTOMERS ........................16 2.3. DIFFERENT STRATEGIES FOR IMPLEMENTING MULTI-SERVICE NETWORKS .............17

2.3.1. The principles of ATM networks ...................................................................17 2.3.2. Multiple services for IP networks..................................................................18

2.4. IMPLEMENTING MULTIPLE DATA TRANSMISSION SERVICES: PROBLEMS AND SOLUTIONS....................................................................................................................21

3. REVIEW OF STUDIES MADE IN THE RESEARCH AREAS OF THE THESIS..........................................................................................................................................23

3.1. AIMS OF ASSURED SERVICES ..................................................................................23 3.2. ASSURED SERVICES AND ENSURING OF QOS IN IP NETWORKS...............................24 3.3. CONGESTION CONTROL SYSTEMS FOR ASSURED SERVICES IN B-ISDN ..................26 3.4. PRICING ISSUES......................................................................................................29 3.5. SOFT-COMPUTING SOLUTIONS FOR DATA NETWORKS ............................................32

3.5.1. Introduction of soft-computing......................................................................32 3.5.2. Overview of soft-computing solutions for network control problems............37 3.5.3. Use of soft-computing methods for the control tasks of this thesis................38

4. DEVELOPED CONTROL SYSTEMS FOR ASSURED SERVICES ...................41 4.1. THE EARLY STUDY .................................................................................................42 4.2. MOVING CONTROL OPERATIONS TO EDGES OF NETWORKS .....................................46 4.3. PREDICTION OF THE DATA RATES OF THE HIGH PRIORITY DATA FLOWS..................48 4.4. COMPARISON OF DIFFERENT RATE CONTROL SCHEMES ..........................................51 4.5. THE SUMMARIZED RESULTS OF THE STUDIES .........................................................52

4.5.1. Manually tuned fuzzy controllers ..................................................................52 4.5.2. Characteristics of the network environment..................................................53 4.5.3. Significance of the parameters of the controllers..........................................54 4.5.4. Performance of different controllers .............................................................54 4.5.5. What kind of controller should be selected? .................................................56

5. PRICE SETTING AND SERVICE SELECTION PROBLEMS OF THE ISPS ..58

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5.1. COMPARISON OF DIFFERENT SOFT-COMPUTING TOOLS FOR THE CONTROL TASK....60 5.2. THE LATEST STUDIES .............................................................................................63 5.3. THE SUMMARIZED RESULTS OF THE STUDIES .........................................................66 5.4. DISCUSSION OF THE PROBLEMS AND THE SOLUTIONS.............................................68

5.4.1. Auction-based solution for ISPs....................................................................69 5.4.2. Automatic selection process for customers ...................................................69

6. CONCLUSIONS.........................................................................................................70 7. SUMMARY OF PUBLICATIONS ...........................................................................72

7.1. OVERVIEW OF PUBLICATIONS ................................................................................72 7.2. AUTHOR’S CONTRIBUTION TO THE PUBLICATIONS .................................................75

BIBLIOGRAPHY...........................................................................................................77

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List of Publications [P1] J. Harju, K. Pulakka: Optimisation of the Performance of a Rate-based Congestion Control System by Using Fuzzy Controllers. 18th IEEE International Performance, Computing, and Communications Conference, IPCCC 1999. Phoenix/Scottsdale, Arizona, U.S.A. February 10-12, 1999. pp. 192-198. [P2] J.Harju, K.Pulakka: Fuzzy Logic Rate Control for the ABR Service Category in B-ISDN Networks. 1999 American Control Conference, ACC99, San Diego, California, USA, June 2-4, 1999. pp. 4446-4450. [P3] K. Pulakka, J.Harju: Optimization of the Utilization of a Packet Switched Backbone Network by Using a Control System for Low Priority Traffic, 19th IEEE International Performance, Computing, and Communications Conference, IPCCC 2000, Phoenix, Arizona, USA, February 20-22, 2000, pp. 124-131. [P4] K.Pulakka, J.Harju: Distributed Control System for Low Priority Controllable Traffic in Packet Switched Backbone Networks, Networking 2000, IFIP-TC6/European Commission International Conference, Paris, France, May 2000, pp. 596-607. [P5] K. Pulakka, J. Harju: Efficiency of the prediction of high priority traffic in enhancing the rate based control of low priority traffic. Smartnet 2000 conference, Telecommunication Network Intelligence, Vienna, Austria, September 18 - 22, 2000, pp. 181-196. [P6] K. Pulakka, J. Harju: Comparison of Different Congestion Control Strategies for Low Priority Controllable Traffic in Packet Switched Backbone Networks. The International Journal of Communication Systems, Vol 14, John Wiley & Sons, Ltd., 2001, pp. 813 - 836. [P7] Pulakka, K. Performance of Two Different Soft-Computing Based Controllers for Service Selection and Price Adjusting Task of the ISPs of the DS-enabled Internet. Proceedings of the 2002 IEEE International Symposium on Intelligent Control, October 27-30, 2002, Vancouver, Canada. pp. 282-289.

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[P8] Pulakka, K. A Dynamic Control System for Adjusting Prices and Quality of Service in DS-enabled Networks. Proceedings of Conference on Network Control and Engineering of QoS, Security and Mobility (Net-Con 2002), October 23-25, 2002, Paris, France, pp. 241-252. [P9] Pulakka, K. Controlling of satisfaction of the end-users and profits of the ISPS in the DS enabled Internet. Proceedings of the 8th International Conference on Communication Systems, ICCS 2002, November 25-28, 2002, Singapore. 7 p.

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List of Main Symbols and Acronyms ABR Available Bit Rate AF Assured Forwarding ANFIS Adaptive-network-based-fuzzy inference System AR Auto Regressive ART Adaptive Resonance Theory ATM Asynchronous transfer mode B-ISDN Broadband - integrated services digital network BRM Backward Resource Management (cell) CBR Constant Bit Rate DMRCA Dynamic Max Rate Control Algorithm DS Differentiated Services EF Explicit Forwarding EFCI Explicit Forwarding Congestion Indication EPRCA Enhanced Proportional Rate Control Algorithm ER Explicit Rate ERM Edge Resource Management EU IST European Information Society Technologies program FALCON Fuzzy Adaptive Learning Control Network FECN Forward Explicit Congestion Notification FL Fuzzy Logic FRM Forward Resource Management (cell)

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FTP File Transmission Protocol GA Genetic Algorithms GFR Guaranteed Frame Rate GMPLS Generalized Multi-Protocol Label Switching IETF Internet Engineering Task Forse IP Internet Protocol IPPM IP performance metrics working group of IETF ISP Internet Service Provider ITU International Telecommunication Union LAN Local Area Network LMS Least Mean Square LSE Least Squares Estimate LSP Label-Switched Paths MLP Multi-Layer Perceptron MMPP Markov-Modulated Poisson Process NC Neuro Computing NN Neural Network nrt-VBR non-real-time Variable Bit Rate NSIS Next Steps in Signaling working group of IETF PC Probabilistic Computing PHB Per-Hop-Behaviours PMP Paris-metro-pricing RBF Radial Basis Functions

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RED Random Early Detection RFC Request for Comment RLS Recursive Least Square RM Resource Management RSVP Resource reservation protocol RSVP-TE Resource Reservation Protocol – Traffic Engineering RTT Round Trip Time rt-VBR real-time Variable Bit Rate SC Soft Computing SGA Simple Genetic Algorithm SLA Service Level Agreements SLS Service Level Specification SOM Self-Organized Maps TCP Transmission Control Protocol TE Traffic Engineering UBR Unspecified Bit Rate VCI Virtual Connection Identification VPI Virtual Path Identification WAN Wide Area Network

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List of Figures Fig. 1. Interests of different communication parties Fig. 2. The three principal data transmission service types Fig. 3. Principles of the DS model. Fig. 4. Various mechanisms for ensuring QoS. Fig. 5. Principles of ABR rate control protocol. Fig. 6. Relationships in pricing Fig. 7. A typical structure of controllers of the first category. Fig. 8. Layout of the control system in the second development step. Fig. 9. A problem of too simple prediction. Fig. 10. Construction of input-output data pairs. Fig. 11. Performance in different network environments. Fig. 12. Average buffer occupancy levels. Fig. 13. Average throughput values. Fig. 14. Arithmetic operations per second per controlled route. Fig. 15. The environment of the studied management system. Fig. 16. The state-changing diagram of the controller. Fig. 17. Fields of the automatically tuning controller. Fig. 18. The control procedure. Fig. 19. The structure of the controllers in the latest studies. Fig. 20. The first example of the price-setting and service selection strategy. Fig. 21. The second example of the price-setting and service selection strategy.

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Fig. 22. The layout of the controller which make service selection decisions for end-users.

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1. INTRODUCTION Communication between people is a natural psychological human need. In addition, people have always needed communication just for surviving. For these reasons, communication has been, and will be, an important part of human live. It could also be argued that the communication needs of people have very few limits. As an exaggerated example, if some technical equipment could bare one’s thoughts to other people, this equipment would surely be used for several different situations. This aspect of the human character has also affected the development of communication networks. New technical innovations have effectively been used for implementing larger, faster and more scalable networks than old ones. Since the late 80’s, the research community and commercial companies have worked to develop multi-service networks. A natural reason for this development issue has been the rapid development of computer technology. Today, the newest mobile terminals (e.g. mobile phones and pocket computers) can run multimedia applications. In this situation, the need for multi-service networks is obvious. They should offer different services for different network-oriented applications according to various requirements of the applications. Some applications, like video and audio applications, require very stable transmission services with strict delay and delay variation guarantees. Some other applications, like a file transmission, benefit from high data transmission rates and small packet losses. In addition to the quality of service (QoS) issues, network operators should also consider financial issues. The operators should find a suitable balance between prices of services, implementation costs of services and quality of data transmission services.

The development of technically and financially feasible multi-service networks has been a challenging task. The history of multi-service networks includes several different approaches. For example, asynchronous transfer mode (ATM), resource reservation protocol (RSVP) and the differentiated services (DS) model are all different approaches for implementing multi-service networks. Some approaches, like ATM, have been technically satisfactory, but they are not commonly used for end-to-end communication for financial reasons. On the other hand, some other schemes, like RSVP, have some principal technical weaknesses, such as bad scalability. Today, intensive research work is still going on in the area of multi-service networks. Research groups are developing new multi-service approaches and combining the existing schemes for finding better solutions. It seems that it is relatively difficult to find technically and financially satisfactory multi-service schemes which would also be commonly accepted. For these reasons, it could be argued that QoS will be a very justifiable research area also during the coming years.

In addition to QoS, pricing of data transmission services of multi-service networks is another complex and important research issue. Pricing is important for both customers and network operators. Customers benefit from low service prices. It could be said that cheap or even free services, whose quality is reasonable, would be satisfactory for customers. At least, prices of services should correspond with quality of services. It can also be expected that customers compare services and prices of different operators.

The principal target of operators is to maximize their financial profits. Pricing of data transmission services affects their profits directly. In the open market environment, the operators should consider that the relation of quality and prices of services offered is

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competitive compared to other operators. Operators should consider several issues. What arguments are used for charges to customers? What are the price differences between different services? How often are prices changed? What is the duration of agreements established with customers? How are changes in the market environment measured? How is the satisfaction of customers measured? At least these issues must be resolved in implementing pricing strategies. During recent years, several different pricing strategies have been developed. It can be assumed that pricing will be an important research issue also in the future.

From the technical perspective, multi-service data networks need various traffic control mechanisms to ensure proper services for applications. Ideal operation of the mechanisms would require accurate information about transferred data flows and available resources of all networks between user terminals. However, control mechanisms implemented for real networks have rarely exact information about these issues. Because of data transmission delays of networks, control operations are often based on old information about states of networks. On the other hand, it is questionable how much and what information network operators are willing to share with each other. In addition, the computation capacity of network components is limited. Network components are designed for transferring data, not for performing time-consuming calculations. For this reason, the degree of complexity of calculations that control mechanisms can contain, and what components perform these calculations, should be carefully considered. The control mechanisms should also be scalable for use in different sizes of networks.

This thesis concentrates on two different quality of service (QoS) related control tasks of multi-service data networks. In the first part of this study, different approaches for solving the well-known congestion control problem of assured service in the broadband integrated services digital network (B-ISDN) [1] type of networks are studied. Different control systems are developed for optimizing use of the available resources of the networks, after the load of higher priority data flows. In the second part, management of the dynamic SLAs (Service Level Agreements) [2] of the ISPs (Internet Service Providers) in the DS-enabled IP (Internet Protocol) network environment is studied. The researched systems select DS classes for data flows and set prices of SLAs so that defined aims of ISPs are achieved. The systems are designed for an open and dynamic market environment in which the customers are able to select the SLAs freely between services of different ISPs.

In both control tasks of this thesis, controllers do not have exact information about the issues that affect the control results. In the first task, a control system should be capable of working with delayed information about the load of network nodes and characteristics of traffic flows. In the second task, SLA management systems of ISPs do not have information about services and prices of other network operators. Scalability, reliability and usability of the mechanisms developed have been important research items in both studies. Network control mechanisms should be scalable for a large number of users so that the mechanisms can be used in different network environments. Obviously, they should also perform sufficiently reliable control decisions in all load situations. Usability is an important characteristic for network operators.

In the present study, various soft-computing (SC) (fuzzy logic, neural networks, genetic algorithms) based control schemes are developed for the control tasks studied.

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The reason for using these tools is their suitability for control situations where controllers do not have exact information about the systems controlled. Soft-computing methods have been used for various tasks, from control of transportation systems to aircraft applications. During the 1990’s, soft-computing methods were also popular tools for solving various network control tasks. Most network control tasks must be done without exact information about the control environment.

The principal contribution of the first part of this thesis is to research the suitability of different soft-computing control schemes for the studied congestion control task. The important questions are: 1) how complexity of the controllers affects the control results, 2) which network components should perform the control computations and 3) how is the control information transferred between the performing components. Complexity of the developed controllers varies from simple manually tuned fuzzy controllers to automatically tuning neuro-fuzzy control schemes. Suitability of user terminals, and different nodes of networks for control computations is studied.

The principal contribution of the second part of this thesis is to develop soft-computing based SLA management systems which are able to consider the opinions of the customers regarding suitable combinations of quality and price of transmission services. Soft-computing schemes are suitable control tools for highly non-linear dynamic systems, such as the studied service selection process of the network users. The important design issues are: 1) how the opinions of the customers are measured and described, 2) how the control targets of the ISPs are described and 3) how the control systems can react to dynamic changes of the studied market environment.

The rest of this thesis consists of the following chapters. Chapter 2 gives an introduction to implementation strategies of multi-service data networks. Chapter 3 describes the existing research work done in the areas of the thesis. In Chapters 4 and 5, the research work of this thesis is described. Chapter 6 includes conclusions of the thesis and Chapter 7 introduces publications of the thesis.

2. MULTI-SERVICE NETWORK ENVIRONMENTS This chapter describes the principle characteristics of B-ISDN networks and different solutions for implementing multi-service IP networks. These network environments are especially important for the present study. Interests of communication parties and technical solutions of the networks are described. The chapter also defines the network control problems studied in this thesis and introduces different solutions for solving problems.

2.1. Interests of different communication parties

The communication parties of current data communication networks can be roughly divided into buyers and sellers of data transmission services (see Fig. 1). The end-users are pure buyers who only want to buy services for their various data transmission needs. Network operators act as both buyers and sellers. They offer services of their networks to end-users and other operators, and they also buy services from other operators.

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The end-users are interested in the relationship of quality and prices of services. The quality of the services should correspond with QoS requirements of network-oriented applications. A well-known fact is that QoS requirements vary between different applications. Data transmission applications (e.g. e-mail services and FTP) benefit from large data transmission rates and small packet loss ratios. On the other hand, video and voice applications (e.g. video-conferencing, voice calls, on-demand movies) benefit from small transmission delay and delay variation values. In addition to the technical requirements, the opinions of different end-users concerning acceptable services vary according to their personal interests and different transmission needs. For example, companies may select expensive and high quality services for Internet traffic, while home users may accept worse QoS with lower costs. Fig. 1. Interests of different communication parties

The operators of commercial data networks are mostly interested in their financial profit. The financial profit from selling services to end-users and to other operators should correspond with the expectations of the operators. In fact, it can be assumed that most operators would like to maximize their financial profits. Achieving this aim requires that the relationship between quality and prices of the services offered should be suitable

Operators selling their data transmission services

Buyers of data transmission services (End-users and operators)

Operators would like to maximize their financial profit.

Services offered should correspond with the expectations of customers

Technical quality of bought services should correspond with QoS requirements of network-oriented applications.

Relationship between QoS, implementation costs and selling prices of services

What kinds of technical solutions are used in the networks?

Service agreements with end-users and other operators should be established carefully.

Prices of bought services should be minimized.

Implementation costs of the data transmission services offered should be optimized.

Prices of services offered should be compatible.

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for the potential customers of the operators. For this reason, operators should both design their networks and establish the service agreements carefully. The agreements established with other operators are important, because the transferred data of the customers usually flows through the networks of several operators. End-to-end quality of the services depends on the quality of services of all networks which transfer data between source and destination terminal [3]. Agreements between operators are needed for negotiating certain QoS for data flows transferred between the networks. From another perspective, the costs of the agreements affect also the selling prices of the services of the networks.

2.2. Principal issues in offering proper services to customers Types of services. The commonly accepted opinion is that data networks need several different transmission services for serving applications of end-users properly. Data transmission services in multi-service networks can be roughly classified as high priority (also called expedited), assured and best-effort services. The high priority services are designed for real time applications (e.g. video and audio applications), which require strict transmission delay and delay variation guarantees. The data flows of assured and best-effort services can use the available transmission capacity of the network, after the load of the high priority data flows. The applications using assured and best-effort services are traditional data transmission applications, which are able to adapt to changing transmission capacity of the networks. They do not require certain data transmission delay and delay variation guarantees, but they benefit from large data transmission rates and small packet (cell) loss ratios. However, assured and best-effort services do not guarantee any specific data transmission quality. The principal capacity-sharing policy is also described in Fig. 2. Fig. 2. The three principal data transmission service types

Separation of services. The proper implementation of different services requires that the networks should be able to separate the services. A separation requires use of 1) traffic

Load of high priority traffic

Capacity

time

Data flows of assured and best-effort services are allowed to use free transmission capacity left over data flows of high priority services. Operators may also reserve some capacity, especially for best-effort traffic.

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classification, 2) queuing and 3) scheduling mechanisms. The traffic identification mechanisms identify the traffic flows of different services. In this way, the nodes of the networks are able to separate traffic of different services. Defining different queues for data flows of different services is a common way to separate the data flows of various services. Nodes of the networks include queue systems for each output link. Data packets transferred using different services are stored in different queues. The scheduling mechanisms are used for defining the traffic transmission order between defined services in the nodes of the networks. Needed control operations. In addition to sharing the transmission capacity of networks between different services, networks must also control data flows using the same services. The control operations should ensure that the QoS offered for data flows corresponds with the QoS definitions of the established service agreements. The required control operations depend on the types of data transmission services. High priority services need more control operations than assured services, because QoS definitions of high priority services are more strict. Best-effort services do not use any network layer level control operations. However, data flows of best-effort services can be controlled using some upper-layer application dependent protocols.

High priority services require use of admission control and ‘behaviour control’ mechanisms. Admission control mechanisms decide if networks accept or reject new data flows into their networks. Networks cannot serve new data flows if this would cause problems in serving existing data flows. ‘Behaviour control’ mechanisms are needed for monitoring behaviour of the parties sending the data flows (source terminals or other networks). Characteristics of the data flows injected into the networks should correspond with the definitions of the established service agreements.

Assured services always include some control mechanism which tries to control the data rates of data flows so that the available transmission capacity for services is used optimally between data flows.

2.3. Different strategies for implementing multi-service networks There is no single perfect strategy for implementing multi-service data networks. For

this reason, it is obvious that various approaches have been developed. The developed approaches can be roughly distinguished according to the connection types and traffic control strategies used. These selections affect both performance and scalability. In this section, ATM networks and various strategies for implementing multiple services for IP networks will be introduced.

2.3.1. The principles of ATM networks

The historical background of ATM networks is circuit switched telephone networks. These networks ensure QoS for calls by reserving end-to-end constant bit rate circuit based connection for each call. However, constant bit rate circuit switched networks

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cannot serve all recent network-oriented applications in a cost-effective way because of the variety of transmission needs of the applications. For this reason, ITU (International Telecommunication Union) [4] defined the B-ISDN model for multi-service packet switched networks.

ATM networks follow roughly the principles of the B-ISDN model. The data transmission in ATM networks is connection-oriented and nodes of ATM networks use a cell switching mechanism for transferring cells from input links to output links. The connection-oriented transmission ensures that ATM networks are able to control individually each connection. During the connection establishment process, a routing protocol defines paths for the connections according to defined QoS principles for the connections. Special service class dependent control mechanisms control each active connection so that QoS requirements of all connections would be fulfilled optimally.

The principal advantage of the cell switching strategy is effective cell transmission operation. Cells of the connections can be transferred fast between input and output ports. In ATM networks, the cell switching strategy is based on the virtual connection identification (VCI) and the virtual path identification (VPI) values. Every transferred cell of the connections includes the transmission link dependent VCI and VPI values. ATM nodes can define the output links and new VCI and VPI values for ATM cells of input links using their switching tables. The tables are updated for each new connection during the connection establishment processes.

ATM-Forum has defined six different service categories for the ATM layer of ATM networks [5]. These categories follow the broad B-ISDN traffic class definitions of ITU. The categories are named constant bit rate (CBR), real-time variable bit rate (rt-VBR), non-real-time variable bit rate (nrt-VBR), unspecified bit rate (UBR), available bit rate (ABR) and guaranteed frame rate (GFR) service categories. The CBR and the rt-VBR service categories are designed for real-time traffic and the other categories can be used for non-real-time traffic. The service categories are defined using combinations of the traffic and the quality parameters. The traffic parameters describe characteristics of data flows and the QoS parameters describe the required quality of service for data flows. Using the terminology described in Section 2.2, the CBR and the rt-VBR service categories could be named the high priority services, while the ABR and the GFR services belong to a group of assured services. The UBR service category offers best-effort services to customers.

In summary, ATM networks offer all needed services for different transmission needs. They are able to transfer voice traffic, video traffic and data with different QoS guarantees. However, ATM technology did not become the popular standard for end-to-end communication between user terminals. Currently, ATM technology is mainly used for large core networks.

2.3.2. Multiple services for IP networks The IETF (Internet Engineering Task Forse) [6] has developed mechanisms for

multiple data transmission services of IP networks. The RSVP (Resource Reservation Protocol) is the approach for implementing the integrated service model for IP networks.

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In this model, network nodes include the special functions for reserving the transmission capacity for the data flows. The resources for data flows are reserved using a special signalling protocol prior to a transmission phase [7]. In this way, data transmission in RSVP-enabled IP networks is no longer connectionless. The strong point of the RSVP protocol is that it is able to reserve resources for individual data flows so that end-to-end QoS requirements of individual flow are fulfilled. Bad scalability is the main weakness of the protocol [8]. Every router of a network must handle information about all active data flows in a network. For this reason, the protocol is quite badly suited for large networks. However, the RSVP can be used successfully in relatively small networks where the number of data flows is not too great compared to the computation capacity of the routers. The differentiated services model. The bad scalability of the RSVP indicates that the principal advantage of IP networks is simplicity. The RSVP is not scalable because it is too complex a scheme for the traditionally simple Internet architecture. This fact was noticed in the development of the differentiated services (DS) model. In the DS model, the core routers just transfer traffic according to service definitions (codepoint values) of data packets and the ingress routers select services and perform the policy functions for the incoming data flows (see Fig. 3). In this way, the ingress routers perform the most time-consuming tasks. Network operators establish the special service level agreements (SLAs) with their customers (end-users and other operators). These agreements define costs and expected QoS levels for the data flows [3]. The operators are responsible to their customers for ensuring that the end-to-end services offered correspond to the QoS definitions of established SLAs at least at some statistical level.

Different services of the routers are implemented using queue systems, called per-hop-behaviours (PHBs) [9]. Each output port of the routers includes a set of PHBs for different services, and the traffic scheduling rule for queues of the PHBs. Lengths of the queues, the scheduling rules of the defined PHBs and the traffic management functions of the ingress routers define how good QoS data packets of different DS classes can get. The definitions of the IETF for the DS model do not limit implementation of the PHBs of the routers. Operators of the networks are allowed to define the PHBs for the routers freely according to their individual targets.

The IETF has developed two different PHBs for the DS-enabled networks. The defined PHBs are the EF (Expedited Forwarding) [10] and the AF (Assured Forwarding) [11] PHBs. These PHBs are just proposals of the IETF for developing different services. The EF (Explicit Forwarding) PHB has been developed for implementing high quality services which offer low delay, low jitter and low packet loss services. EF PHB is a good PHB for implementing high priority services for networks.

The target of the AF (Assured Forwarding) PHB is to create various services which offer different QoS for the customers of the networks. The PHB includes four service classes, which all include three different packet drop preference levels. The operators can set different QoS levels for the AF service classes by defining the data transmission resources (buffer lengths, service rates) differently for different classes. The packet drop preference levels define the packet discard probabilities inside the AF classes. AF PHB implements kinds of ‘better than best-effort’ service for IP networks.

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Distribution of time-consuming data flow related control operations in the ingress routers of the networks makes the DS model scalable also for large networks. In addition, the model enables implementation of different services. The operators can guarantee a quality of services of their own networks at least at some probabilistic levels, because they can freely control their networks. However, estimation of quality of end-to-end data transmission services is a more complex task, if data packets are routed through IP domains of several network operators. Firstly, the DS model does not provide any kind of data flow-based control or resource reservation functions through all networks used for transferring of data packets. Secondly, network operators are not willing to share all (if any) information about resources and loads of their networks with other operators [12]. In this situation, it is important to research what kinds of agreements can be established between operators, what kinds of traffic control operations are needed between networks and what kinds of services can be guaranteed to end-users.

Fig. 3. Principles of the DS model.

Each output port of the routers can include a set of PHBs for different services and the traffic scheduling rule for queues of the defined PHBs. Transmission speeds of links, lengths of the queues and scheduling rules affect packet transmissions through the routers.

Ingress routers Core routers End-users

1) Traffic policy functions for controlling QoS

2) Selection of DS classes (DS codepoints) for traffic coming from user terminals or other domains.

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2.4. Implementing multiple data transmission services: problems and solutions

Achieving the theoretical optimal performance of the multi-service networks would

require that the networks have exact prior information about data transmission resources and their utilization. In this situation, it could be possible to share the data transmission resources of the networks optimally. Unfortunately, it is simply impossible to know the timing and characteristics of data flows of end-users exactly. Network operators cannot assume that end-users would report all their data transmission needs beforehand and that end-users would know exactly what kinds of traffic their applications would generate. From this perspective, it is important to consider what information is available for the control mechanisms. Traffic control and network management functions can be based on the use of the traffic source models, the queue models, simulations and measured information about the behaviour of the traffic sources and the load of the network components. The traffic source models, like the fluid source model for packet voice [13] and the markov-modulated poisson process (MMPP) [13], can be used for modeling the behaviour of different traffic sources. The models are commonly used for estimating the transmission capacity needs of the voice and the VBR video sources. The results of these models can be used for dimensioning capacities of the network components and for the admissions and access control mechanisms. However, the models are typically designed for specific data sources, and achieving proper results of the models requires statistical information about the behaviour of end-users and traffic sources. Furthermore, it has been found that the behaviour of many applications is self-similar (see e.g. [14]) and the traditional traffic source models cannot model traffic precisely (see e.g. [15]). For these cases, the special self-similar models are used. Operators should consider carefully what traffic source models are used and how exact results the models are able to give. Queue models. The queue models are also important tools for designing the traffic schedulers for the routers (or switches) of the networks. When the traffic source models describe the behaviour (or load) of different sources, the queue models describe the behaviour of the queue systems. The queue models give the needed information to the operators for dimensioning buffers of different data transmission services and for designing the data packet handling strategies of the schedulers. For using the queue models, the operators should know the characteristics of incoming traffic to the schedulers, the QoS targets of different services and the traffic transmission capacities of routers and links between the routers. Simulations mimic operations of real systems. Simulations give overall information about the operation of systems. In practice, developers define simulation models and simulation algorithms simulate the systems. Simulations are especially well suited for verifying the operation of systems before real systems are run. Operations of systems can be developed and tested in a cheap environment before implementation of the real systems. The use of simulations saves money and time.

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The exactness of simulation models is an important design issue. Precisely designed simulation models give more exact information about the real systems than imprecisely designed ones. However, several realities affect the suitable levels of precision of models. First, only the known characteristics of the real systems can be modeled. For example, the behaviour of sources in communication networks cannot be known exactly. Second, all characteristics of systems need not always be modeled. In some cases, even rough simulation models may give enough information about the systems. Third, implementation costs and duration of simulations increase as a function of precision in modeling. Designers should find a suitable compromise between costs and the need for information.

Discrete-event simulations (see e.g. [16]) are commonly used for network protocols. They suit for the simulation task well, because the operations of network protocols can be easily represented using events. Events describe operations of the protocols. For example, transmission of a data packet over transmission link can be described as an event. Simulation models define the duration of events and how the events affect the generation of other events. Simulation time changes according to the duration of individual events. In this way, it could be said that discrete-event simulations describe the operation of systems as a flow of individual events. Measurements. Control of networks would be easy if traffic and queue models gave sufficiently exact information for control operations. Unfortunately, this is not always the case. The measured information about traffic sources and the load of networks can be used in these difficult cases. The measurements can be used in many different ways.

First, some mechanisms may calculate statistical information using the measurements and use this information for the control operations. For example, statistical information about the behaviour of traffic sources and occupancy levels of the buffers could be used for admission control operations and long-term management of networks.

Secondly, the measurements can also be used for iterative control operations. For example, periodic measurements of occupancy levels of the buffers of the assured services can be used for controlling the data sending rates of the source applications. In this scheme, the changes between the consecutive measurements indicate how the available capacities for the services vary and the data rates can be adjusted iteratively using this information. The principal advantages of the iterative control operations are their computational simplicity and good adaptability. The iterative controllers use observations about states of networks as an input of control decisions. The performance of these controllers does not depend on the correctness of any pre-defined model. However, the performance of the controllers depends on data transmission delays. In many control cases, measured information for control actions must be transferred between network components. For this reason, the control decisions must be made using the old input information. It must be considered what observations are used for the iterative control mechanisms, how long it takes to transfer the control information from measuring components to the performing network components and how much the transferred control information loads the networks.

Thirdly, the measurements are also used for various prediction-based control mechanisms. Although it is impossible to know the future events of networks exactly, short-term predictions of different events have been found to be useful for traffic control

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mechanisms. The principal benefit of the ‘right’ predictions is that they decrease the bad effects of data transmission delays in network control mechanisms. For example, the performance of a data rate control mechanism of the assured service can be increased by predicting the buffer occupancy levels of the service in the near future. Prediction algorithms predict future events of networks using measured past events. For this reason, it must be considered carefully what events are measured for the prediction algorithms and how well these measurements describe characteristics of the controlled processes. All above introduced mechanisms and tools have their own targets. Usually, they all are needed for implementing properly working multi-service networks. Traffic control and network management functions perform different calculations. However, the computation capacity of many network components is limited. For this reason, location and complexity of computations are important design issues. In addition, operators should also have an easy interface for managing control mechanisms. The factors of management mechanisms should be understandable for the operators, and changes in the factor values should have an unambiguous impact on the operations of the mechanisms. In the optimal case, the control mechanisms would operate adaptively. The operators would just define their targets, and the mechanisms would find the ‘right’ operations for fulfilling the targets.

3. REVIEW OF STUDIES MADE IN THE RESEARCH AREAS OF THE THESIS

This chapter describes existing research carried out in the focus areas of the thesis. The aims and the control problem of assured service are described in Section 3.1. Section 3.2 describes the mechanisms researched for ensuring end-to-end QoS in DS-enabled IP networks. In Section 3.3, mechanisms for implementing assured services in B-ISDN networks are discussed. Pricing issues of multi-service networks are described in Section 3.4. Finally, soft-computing systems and their use in solving network control tasks are discussed in Section 3.5.

3.1. Aims of assured services The aims and the requirements of assured services can be studied from the user’s and

the operator’s points of view. From perspective of users, the controllable load service should ensure optimal transmission rates and minimal packet loss ratios. Another important issue for the users is fairness in the sharing of available resources. If the transmission costs for users are equal, a user expects to receive equal transmission services compared to other users of the same service.

From the perspective of the network operators, assured services belong to low-priority services, which cannot be charged heavily. Although the charging of different services is a very complex issue, it can be said that the transmission charges for low-priority non-

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real-time services must be significantly lower than those for high priority services. On the other hand, it has been noticed that most of the traffic in future networks will still be best-effort traffic [17]. Because many flexible applications using best-effort services could also use assured services, it can be assumed that the volume of the traffic of assured services may be high. For this reason, it is reasonable to develop effective assured services which ensure that the available capacity of the network is used optimally. However, the implementation costs of assured services cannot be too high because of the low prices of the services. In summary, some kind of compromise between good technical performance and cost efficiency could be an optimal solution for these services.

3.2. Assured services and ensuring of QoS in IP networks In IP networks, assured services have been implemented in various ways. In the resource reservation method (like RSVP), a special protocol reserves the needed transmission capacity for each active controlled data flow. The reservation is done according to individual transmission needs of the controlled data flows. Nodes, which transfer the data packets of the data flows, ensure that the reserved transmission capacities exist. The protocol can also reject data flows if a network has not enough transmission capacity. The sources of controlled data flows are not allowed to send more data to the network than specified during the resource reservation scheme.

The differentiated services model is another method for implementing assured services in IP networks. In DS model, assured services can be implemented using the PHBs of assured forwarding (AF) service class (see Subsection 2.3.2) or using some special PHBs. These PHBs define the transmission priority of packets on output ports based on the DS codepoint values of packets. In this way, each output port includes logical rules for sharing transmission resources between different services, and the load of output ports defines the real sharing of resources between data packets. The principal idea of assured service is implemented locally at output ports, and global implementation is the sum of the local implementations. This kind of distributed control for implementing assured service makes the DS model scalable, but it also requires additional mechanisms for ensuring end-to-end QoS, at least at some statistical level.

During past years, ensuring end-to-end QoS in IP networks has been a heavily studied research issue. Large institutes have organized their own studies. For instance, researchers working on the IETF and EU IST programs [18] have studied the issue from various points of view. IETF NSIS (Next Steps in Signaling) working group concentrates on QoS signaling for the Internet. For example, interworking between different domains with various QoS schemes has been studied. The working group has published some Internet-drafts (see [19]-[23]). There are also some studies, like RSVP-TE (Resource Reservation Protocol – Traffic Engineering) [24] and GMPLS (Generalized Multi-Protocol Label Switching) [25], which try to use RSVP protocol effectively in the recent network environment. For example in RSVP-TE study, RSVP protocol is used for establishing label-switched paths (LSPs) in MLPS enabled networks. The use of MLPS in DS-enabled networks has been seen as a promising traffic engineering (TE) approach. MLPS and TE working groups of IETF are both studying this issue. It has been argued (see [26]) that MLPS as a path-oriented scheme is faster and more reliable than hop-by-

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hop data transmission. MLPS implements a similar label-switched data transmission for IP networks as was already developed in ATM standardization. When MPLS is used in DS-enabled networks, MPLS ensures suitable paths through networks and DS functions in routers ensure high priority packet transmission in the routers.

In addition, different research groups have developed various resource reservation schemes, like YESSIR (YEt another Sender Session Internet Reservations) [27], Boomerang [28], INSIGNIA [29], Border Gateway Reservation Protocol (BGRP) [30] and ST-II [31]. IETF Policy Framework and IETF Resource Allocation Protocol working groups have also studied mechanisms for ensuring QoS using the current QoS technologies. The principal aim of the IP performance metrics (IPPM) working group of IETF [32] is to define the standard metrics for describing quality, performance and reliability of data transmission services [33]. For instance, RFCs (Request for Comments) are written for defining one-way packet loss [34], one-way-delay [35] and connectivity measurements between two nodes [36]. A common metrics are essential for inter-domain resource/agreement negotiations.

The three projects of the IST program, Aquila [37], Tequila [38] and Cadenus [39], handle different aspects for offering end-to-end QoS. The Aquila project concentrates on access of end-user applications to QoS services. In the Tequila project, relationships between customers and service providers are studied. The Cadenus project handles definition, flexibility and the dynamic nature of SLAs and SLSs (Service Level Specifications). There are also several studies (see e.g. [3]) which concentrate on QoS measurement systems. These systems try to measure the QoS levels which can be offered for data flows. Principal ways for ensuring QoS are summarized in Fig. 4. Fig. 4. Various mechanisms for ensuring QoS.

Ingress routers: Traffic shaping, Active packet marking, Admission control, Measure of QoS

Inter-domain service agreement negotiation.

DS PHBs in routers of domains.

Construction of MPLS paths through domains

User terminals: Negotiation of SLAs between ISPs, Measurement of QoS, Compliance with the established agreement (in the optimal case)

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3.3. Congestion control systems for assured services in B-ISDN Another way to implement the principles of assured service is to use congestion control (or flow control) methods. The main task of congestion control methods is to utilize the queues of the assured services optimally. The data rates of controlled data flows should be kept as high as possible without danger of packet losses of the controlled data flows. Congestion control systems control the data rates according to the data rate changes of the higher priority data flows.

In today’s networks, perhaps the best-known congestion control protocols are TCP (Transmission Control Protocol) for IP-based networks and the flow control mechanism of the ABR (Available Bit Rate) service category [5] in ATM (Asynchronous Transfer Mode) networks. Although workable congestion control protocols were implemented for real network environments already in the 1980’s and 1990’s, many research groups have developed new protocols or additional capabilities for the existing congestion control protocols (see e.g. [40]-[43]). These research groups have tried to find good congestion control solutions with respect to the optimal load of the network, minimal packet loss ratio, optimal fairness, minimum amount of calculations and the minimal required sizes of the buffers in the network nodes. There are several studies (e.g. [44] [45] [46]) which attempt to increase the performance of the oldest congestion control protocols. Most of the studied congestion control methods have been able to achieve good test results at least in the tested (usually simulated) network environments. These results indicate that the congestion control problem can be solved by several alternative methods, whose characteristics may differ from each other considerably.

As a protocol of the IP networks, the operation principles of the TCP protocol differs from the congestion control schemes of ATM networks. The TCP is the end-to-end acknowledgement-based protocol which forces sources to reduce their data rates if the protocol estimates that packet losses take place in the network. The protocol is totally based on the received acknowledgement messages and the estimation of the round trip times (RTTs) of the network. The network components between the end systems do not take part in the congestion control functions. The operational principles of the TCP protocol are easy to understand and accept. At the time as the TCP was developed, the computational capacities of the routers were noticeably lower than capacities of up-to-date routers. For these reasons, it is understandable that the protocol does not use the routers for defining the data transmission rates of the sources. Furthermore, the calculations performed by source terminals for defining the data transmission rates are few enough to be implemented in different source terminals. For these reasons, the TCP protocol is scalable for different network environments and end-user terminals. The good scalability is perhaps the main reason for the popularity and longevity of the protocol.

This thesis concentrates on the congestion control systems of assured service of B-ISDN networks. The basis of these systems is the ABR congestion control protocol [5] defined by ATM-Forum. The protocol defines behaviours of the user terminals, the content of the special RM (Resource Management) cells and the transmission principles of the RM cells. The protocol enables networks to indicate congestions in different ways, but it does not define how the nodes of the networks should calculate their congestion indications or the optimal data sending rates for the sources [47]. For these reasons, it is obvious that different research groups have developed various congestion control

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systems. However, almost all systems use the ABR congestion control protocol of ATM-Forum as a control protocol for transferring control information between nodes and source terminals.

Principles of the ABR control protocol of ATM-Forum are introduced in Fig. 5. During transmission of data, ABR source terminals send periodically forward resource management (FRM) cells to destination terminals. Destination terminals send backward resource management (BRM) cells back to source terminals. FRM and BRM cells include several fields to carry control information. In addition to FRM cells, switches can inform user terminals about congestions using EFCI (Explicit Forwarding Congestion Indication) bit of data cells. Fig. 5. Principles of ABR rate control protocol. The way to indicate sources to change their data rates and the calculation of the optimal data rates for the controlled data flows are two important development issues, which vary between different congestion control methods. The congestion control systems studied can be divided into 1) the explicit congestion indication, 2) the relative rate marking and 3) the explicit rate type of systems. The ATM switches should implement at least one of the alternative congestion control schemes. Explicit congestion indication and relative rate marking. Explicit congestion indication systems, like FECN (Forward Explicit Congestion Notification) [48], are the oldest congestion control approaches for ATM networks. These systems do not calculate any optimal data rate values for the sources. Notification of the sources about congestion is based on the binary feedback sent to the source terminals. As an extension to explicit

Sources use values of field of the BRM cells for adjusting data rates.

Switches mark values of the EFCI field of the data cells, or the fields of the FRM cells.

Switches are allowed to generate BRM cells, or they can modify values of fields of transferred BRM cells according to the specified rules.

Destination terminals send BRM cells to the sources.

FORWARD RESOURCE MANAGEMENT CELL (FRM cell)

BACKWARD RESOURCE MANAGEMENT CELL (BRM cell)

DATA CELL

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congestion indication systems, ‘relative rate marking’ schemes (see e.g. EPRCA (Enhanced Proportional Rate Control Algorithm) [49], DMRCA (Dynamic Max Rate Control Algorithm) [50]) indicate the source terminals if a network is congested (CI bit) or if sources should not increase their data rates (NI bit). The principal advantage of explicit congestion indication and the relative rate marking schemes is their simplicity [51]. However, it has been noticed that basic explicit congestion indication and relative rate marking schemes cannot share the available capacity of the networks fairly [52][53]. They suffer from the “beat-down” fairness problem. In the “beat-down” problem, the probability of reaching a congested output port increases as a function of the number of nodes on routes of connections. For this reason, the connections that transfer data through a large number of nodes must decrease their data sending rates more often than the connections that transfer data through a small number of nodes. The EPRCA scheme was designed for solving the “beat-down” problem. The performance of explicit congestion indication and relative rate marking systems can also be worse than the more advanced systems [52]. It has found (see [52]) that the methods may achieve good performance in the LAN (Local Area Network) environment, but their performance in the WAN (Wide Area Network) environment is not good. The performance of various congestion control methods is also compared in [53]. According to this study, the performance of the explicit congestion notification method depends strongly on values of control parameters of the method. Explicit data rate. Another, more advanced strategy is to send explicit rate (ER) values to the controllable sources. In this way, the sources can dynamically adapt to the data rate changes of the higher priority data flows, which ensures more optimal capacity utilization of the network than the older methods do. The ER-based systems can be divided into the ‘stateless’ (e.g. ERICA [40], ERICA+ [54]) and ‘state maintained’ systems. The ‘stateless’ systems do not and the ‘state maintained’ systems do control the sources on a per-flow (or per-transmission) basis.

Usually the ER-based congestion control methods define the data rates of the controllable data flows using both the data rate information of the higher priority data flows and the occupancy values of output buffers of the ATM switches. In this way, both of the two main targets of the assured service, i.e. optimal load of the network and minimum packet loss ratio, are considered. However, calculation of exact data rate values for control instructions is a much more complex task than notifying about a congestion in an output port. In the ER-based schemes, the max-min fairness strategy [55] is the most popular scheme for ensuring fair sharing of the available transmission capacities of networks between active ABR data transmissions. However, also other fairness strategies are studied (e.g. [56], [5]). Calculation of control instructions. In addition to various ways for indicating congestions or suitable data sending rates for the sources, there are also several possible schemes for calculating the control instructions for the RM cells. The complexity of the schemes varies considerably. The simplest explicit congestion notification systems may just monitor occupancy levels of queues and set the values of the congestion indication fields of the RM cells or a value of the EFCI (Explicit Forwarding Congestion Indication) bit of the data cells according to the occupancy levels. The sources adjust their data

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sending rates by just simple calculations. In the ER-based systems, data rate values can also be defined by simple calculations, as done in the Erica scheme [40]. Unfortunately, the cost of too simple control operations is imprecise control results. Extremely simple control schemes cannot consider carefully dynamically changing loads of the ATM switches and data transmission delay between nodes and source terminals. These weaknesses cause cell losses, the need for large buffers for the ABR traffic or waste of the available transmission resources. This fact has provided the reason for developing more complex control systems which should achieve more accurate control results.

Prediction of the data rates of the higher priority data transmissions or occupancy levels of the queues of the ABR service seems to be a promising solution for achieving good control results. Several studies have been published in which the behaviour of the VBR traffic or occupancy levels of queues of the ABR service have been predicted using some control-theoretical or computationally intelligent method (e.g. [57]-[60]). The systems based on the control-theoretical solutions usually achieve good test results in terms of the accuracy of the control operations (e.g. [61]). Unfortunately, the complex control systems require also time-consuming calculations, as noticed in [62]. Furthermore, some control-theoretical systems (e.g. [63][64]) require exact information about network environments. These characteristics complicate the use of these systems for real networks.

In academic studies, fuzzy and neural network controllers have also been popular tools for solving congestion control problems (see Section 3.5). The common argument for using these tools has been their capability to adapt to changing traffic load situations without exact information on the behaviour of the traffic. These methods are able to learn the behaviour of the traffic from samples of the traffic, or system experts can define the control decisions of these systems. The use of soft-computing based systems for the rate control task are discussed carefully in Section 3.5.

Today, it seems that traffic control functions in future backbone networks should be located in the edges of the networks [65] or in the source terminals. This development step is a natural result of the increase in the transmission speeds of the networks and the complexity of the control systems. Future core nodes will be optimized for fast data transmission, not for complex computations.

3.4. Pricing issues Pricing of data transmission services is an old research issue. Pricing has been studied for as long as some data networks have existed. For this reason, it seems that pricing will still be an important research item also in the future. Currently, there is no single pricing strategy which would be the best for all network environments and for all operators. For this reason, several alternative pricing strategies have been developed. From the customer point of view, pricing should be simple and enable easy service selections between alternative services. On the other hand, the ISPs expect pricing to be flexible and exact.

Pricing has two fundamental aims. Network operators should set their pricing strategies for charging their customers. The pricing strategies should be designed according to financial aims of operators. On the other hand, pricing can be seen as a mechanism for controlling QoS of different services and for optimizing the use of

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networks. The Paris-metro-pricing (PMP) strategy [66] is a good example of how pricing can be used for controlling QoS. In the PMP scheme, a system includes two different services whose prices differ greatly. The price of the cheap service is low, and the price of the expensive service is very high. It is assumed that most customers do not select the expensive service. In this way, the QoS the expensive service is good, if the operator of the system has just dimensioned the capacities of the services correctly.

The pricing strategies can be divided into the static and dynamic strategies [67]. In the static scheme, the operators set prices of services according to their aims and statistical information about the behaviour of customers. The prices are quite constant. From the perspective of providers, the static pricing strategies are easy to implement. For the customers, the principal benefit of the static pricing schemes is predictability of the data transmission costs. In the dynamic pricing strategy (see e.g. [68]-[74]), the prices of the network services change dynamically according to aims of the service providers and behaviour of the customers. The aim of the dynamic pricing is that the service providers could control the behaviour of their customers by changing the prices of services. For instance, the providers could try to balance the traffic load of the networks or ensure a good QoS for some specific services. The principal condition for using the dynamic pricing is that the customers react to the price changes. Operators can only control use of services by changing prices if the customers react to the price differences.

Different pricing strategies are summarized e.g. in [75]. Flat-rate pricing, capacity-based (flat-rate) pricing, usage-based pricing, congestion pricing, connection-time based pricing, time-of-day pricing, per-packet pricing, Paris-Metro pricing [76] (see above), edge pricing [77], priority pricing [78]-[80], expected capacity pricing [81] and responsive pricing [82] are all different pricing schemes. Next, some of these pricing strategies are introduced.

Flat-rate pricing is the traditional pricing strategy of Internet services. Customers just pay some constant amount of money per month/per year and they get free access to use services [83]. Capacity-based pricing is a special case of the flat-rate pricing scheme. The payments of the customers depend on the capacity of the access links to a network. In usage-based pricing, network service providers offer different services with different prices to the customers. The customers are allowed to select the services according to their personal interests. The service providers change the prices of the services according to their pricing strategies and behaviour of the customers. In this way, the providers can also control service selections of the customers by changing the prices. The principal idea of the congestion-based pricing strategy is that prices of data transmission services change according to congestion levels of networks. In practice, prices of services increase when congestion levels of networks increase. In this way, service providers can control the use of services in the case of congestion. Connection-time based pricing is a very traditional pricing strategy, where the payments of the customers depend on the lengths of the periods of use of the services. This pricing strategy is well suited for services where data rates of transmissions are constant, like in telephone networks. The time-of-day pricing strategy [84] is also a familiar pricing scheme from telephone networks. In this strategy, costs of customers depend on the time of day when the services are used. This pricing strategy allows customers to select the optimal moment of a day when data is transferred. In the per-packet pricing strategy (see e.g. [85][86]), the customers are charged according to the number of transferred packets.

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A large number of different pricing strategies solely indicate that pricing is a very difficult design issue. The complexity of the pricing schemes is an important design item. How complex are the pricing schemes which are really needed and could the network services be charged using simple pricing schemes? Are the customers capable of adapting to the complex pricing schemes? These issues are discussed e.g. in [83]. At first, the author concludes that use of network services is much higher in the ‘flat-rate’ systems than in the systems which use per-packet/per-time based pricing schemes. According to publication [83], revenues of the service providers which used the flat-rate charging where also slightly larger. The author summarizes that the customers are more willing to pay for simplicity than optimize their services. Furthermore, the author argues that it is reasonable to use flat-rate pricing in the Internet.

Advanced pricing systems have their own benefits, if only customers were willing to use these systems. As summarized in [67], dynamic pricing systems can achieve their operational targets. Service providers can really affect the load on their networks using dynamic pricing schemes. However, the advanced pricing operations are not free. Functions for advanced pricing schemes clearly complicate operations of networks. At least, the pricing schemes should take account of relationships between behaviour of the customers, prices of services and QoS offered to the customers (see Fig. 6). Every one of these issues affects the others. For example, if the price of a service decreases, more customers may start using the service, and QoS for individual customers may decrease. The dynamic pricing systems should be able to take these kinds of changes into account.

In summary, there is no single perfect pricing strategy which would be suitable for all networks and for all cases. A lot of research work has been done to develope advanced pricing schemes for optimizing pricing. However, it has also been shown that even the simple pricing schemes may achieve satisfying pricing results. For this reason, it must be considered carefully what kinds of pricing schemes are really needed.

Fig. 6. Relationships in pricing

When QoS of offered services changes, costs of operators also change. For this reason, change of QoS levels should also affect prices of offered services.

Operators should also consider behaviour of customers when they decide QoS of services.

If the prices of services are controlled dynamically, operators should also consider behaviour of their customers in the price- setting process.

BEHAVIOUR OF CUSTOMERS ( end-users, other operators )

PRICES OF SERVICES QoS OF THE OFFERED SERVICES.

Both prices of services and QoS of offered services affect behaviours of customers.

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3.5. Soft-computing solutions for data networks Network control and management operations are done in an environment where information for operations may be noisy, incomplete or totally wrong. In this kind of control environment, the traditional ‘hard’ systems do not achieve optimal operation results. These systems need quite exact information about an environment for optimal operation results. Because of the lack of exact information, simplified models are usually needed for achieving at least some results. Obviously, results of these simplified models are not optimal.

This chapter concentrates on the soft-computing methods and their use for data networks. Soft-computing methods have been seen as suitable schemes for control and decision systems, where the systems do not have enough information about the environments. The chapter includes the following sections. Definition of the soft-computing and various soft-computing methods are introduced in Subsection 3.5.1. Use of the soft-computing methods for network control problems is introduced in Subsection 3.5.2. At the end, use of the soft-computing schemes for network control problems of this thesis is discussed in Subsection 3.5.3.

3.5.1. Introduction of soft-computing The term soft-computing has been defined by professor Lofti A. Zadeh. In his own words, soft-computing systems “exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness, low solution cost and better rapport with reality” [87][88]. The main soft-computing schemes are fuzzy logic (FL), neuro-computing (NC), probabilistic computing (PC) and genetic algorithms (GA) [87]. Different schemes are designed to solve different tasks. In this chapter, fuzzy logic, neuro-computing and genetic algorithms are introduced. Furthermore, hybrid soft-computing systems and the use of soft-computing systems are discussed. The exact algorithms are not represented, because they can be studied in several very good textbooks (e.g. [89][90]). Fuzzy logic. The history of fuzzy logic started in the year 1965 when professor Lofti A. Zadeh published the complete theory of fuzzy sets (see [91]). Since then a great number of researchers have studied the idea of fuzzy logic and its use for various applications. From an engineering point of view, fuzzy logic can be seen as a mechanism for transmitting human knowledge to computer systems. Fuzzy logic is especially suitable for cases where control decisions must be made without exact information about the controlled environment. In these cases, an operational decision must be made on the basis of approximate reasoning and heuristic knowledge about the controlled systems. Fuzzy logic enables the description of heuristic knowledge of system experts and the use of this heuristic knowledge for control decisions.

Fuzzy inference systems [90] are important frameworks for implementing fuzzy logic based controllers. Fuzzy inference systems consist of fuzzy variables, a rule base and a reasoning mechanism. Fuzzy variables describe variables used in the systems. The rule

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base defines fuzzy rules for the systems and the reasoning mechanism performs the inference procedure of the systems. Mamdani, Sugeno and Tsukamoto fuzzy inference systems are three widely used types of fuzzy inference systems. Principles of the fuzzy rules and the inference process vary between these systems.

In the present thesis, Mamdani type of fuzzy inference system is used for implementing various controllers. Mamdani type of fuzzy controllers can include one to several input variables and one output variable. Input variables describe possible states of the controlled process and an output variable describes possible control actions. As fuzzy variables, the variables consist of one to several linguistic values. Linguistic values separate numeric value ranges of the variables to sub-ranges. Linguistic values can be called using commonly understandable names or symbols. A rule base of fuzzy controllers defines control actions of controllers in the defined states of controlled processes. The rule base consists of a set of IF-THEN type of rules which describe the controlled states of the processes and suitable control actions for the states using the linguistic values of input and output variables. System experts can affect the control actions of the controllers by tuning the variables and the rule base of the controllers according to their human control experiences. The controllers can be also tuned using some automatic tuning process. Neuro-computing. The history of neuro-computing (see [92]) is even longer than the history of fuzzy logic. It can be said that the history of neuro-computing began in the year 1943. McCulloch and Pitts developed a network of binary decision units [93]. They argued that this network could implement any logical function. Furthermore, Rosenblatt developed a one-layer perceptron network and used this network for classifying patterns [94][95]. However, it was argued that single-layer networks were not able to solve non-linear classifying problems [96]. For this reason, researchers started to develop multi-layer perceptron networks. Development of the backpropagation algorithm (see [97]-[99]) was the crucial innovation for multi-layer perceptron networks. It solved the parameter setup problem of networks. Since these early days of neural networks, a huge amount of research work has been done to develope different neural network approaches and to implement them for industrial problems.

At present, neural network approaches can be divided into feedforward and recurrent networks. Feedforward networks include single or multi-layer perceptron (MLP) networks, and radial basis functions (RBF) networks (see [89]). Perceptron networks are especially suitable for function approximation. Parameters of networks can be adjusted so that the networks can approximately mimic the input-output value pairs of functions which have been targets of parameter adjusting processes.

Recurrent networks include competitive networks, self-organized maps (SOMs) [100][101], Hopfield networks [102] and adaptive resonance theory (ART) models [103]. Recurrent networks are used for unsupervised learning, associative memory, and self-organization. Genetic algorithms. Research work on genetic algorithms started later than work on fuzzy logic and neuro-computing. In 1970, Holland initiated this research work with his publication ‘The Simple Genetic Algorithm’ (SGA) [104]. The principal idea of genetic algorithms is to mimic the genetic evolution process of nature in order to find optimal

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solutions. Using the principal genetic operations, like iterative selection of the best genes, crossover and mutation, genetic algorithms can seek the optimal solutions according to fitness criteria used in optimization tasks. System experts are allowed to define the fitness criteria. Since the pioneering work of Holland, the idea of genetic algorithms has been the subject of intensive research work performed by different research groups. Genetic algorithms include several design issues, like crossover rate, mutation rate, number of crossover points in genes, selection strategies of genes and encoding principles of genes. All these issues affect the performance of the systems and optimization of these issues should be done individually for each task in order to reach optimal results. For these reasons, the need for intensive research work is obvious. Hybrid soft-computing systems. The principal idea of hybrid soft-computing systems is to use several different soft-computing schemes in systems. The reason for using hybrid systems is simply to use symbiosis of different SC schemes. The ANFIS (Adaptive-network-based-fuzzy inference System) [90][105][106] network is a good example of neuro-fuzzy systems. The ANFIS network is an adaptive network-based fuzzy inference system which can perform the functions of the Sugeno or Tsukamoto fuzzy models. The important characteristic of the network is the capability to learn the behaviour of dynamic systems by training data pairs. The network includes a set of linear and non-linear parameters. The values of the parameters are adjusted by a special hybrid learning rule which is a combination of the gradient method and the least squares estimate (LSE). Furthermore, there are at least 300 publications dealing with the use of genetic algorithms with fuzzy logic [107]. One popular trend has been to optimize linguistic variables and rules of fuzzy controllers using the optimization capability of GA. In this way, GA is used for the tuning task of fuzzy controllers instead of system experts. Genetic algorithms are also used for feedforward neural networks (see e.g. [108]-[110]). For instance, they have been used for the parameter adjusting process of networks and for finding the optimal topology of networks [111]-[112]. Again, the optimization capability of GA was used for improving the performance of systems. Fuzzy systems are also used for controlling parameter adjusting processes of neural networks. For example, there are studies (see e.g. [113]-[114]) where fuzzy controllers are used for controlling the learning rates of neural networks. In feedforward networks, the learning rate controls how fast or how accurately the adjusting of parameters of networks is done. Advanced adjusting of the learning rate can both decrease the parameter adjusting time (faster convergence) and make the ‘learning process’ of networks more strict. In these studies, human knowledge about the parameter adjusting process of neural networks is transferred to NN (Neural Network) systems using fuzzy logic. Use of soft-computing for industrial applications. The principal advantage of SC systems is that they are especially suitable for tasks where exact information about system environments is lacking. It could be argued that very many real-world industrial control tasks are just this kind of problems. From this perspective, it is very understandable that soft-computing methods have been intensively used for solving various industrial problems. The publication [115] of Yasuhiko Dote and Seppo J. Ovaska gives a good overview of the use of soft-computing methods for industrial applications. For example, SC methods have been used for airospace applications,

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communication systems, consumer applications, electric power systems, manufacturing automation, robots, power electronics, motion control, process engineering and transportation applications. There is a huge number of applications, where SC methods can provide solutions. The pioneering works have been done in Japan, South-Korea and Unites States. In Subsection 3.5.2, the use of soft-computing methods for communication networks is described. Problems with soft-computing methods. Although soft-computing methods are useful for many real-world applications, using the methods is not problem-free. The need for an individual design for every system is a common characteristic of all SC-based systems. SC methods cannot be used generically for all tasks without careful adaptation of the methods for current tasks. For example, fuzzy controller and neural network based systems require selection of input and output variables. The input variables must be selected so that the numeric values of these variables are easy to measure and the variables describe well happenings in the system environment. The output variables should be selected so that their meanings (and values) are understandable when the input variables are known. There must be some kind of correlation between values of input and output variables. In manually tuned fuzzy controllers, system experts must tune linguistic variables and rulebases of controllers. Furthermore, the genes of genetic algorithms must be encoded so that the genes describe alternative options in optimized environment well.

SC methods include several parameters and selections which affect the performance of the methods. In fuzzy controllers, system experts must select number of linguistic values of variables, the shape of linguistic values, the number of rules in rulebases and the algorithm used for defuzification. Neural networks include usually at least one factor (usually called the learning rate factor), which controls the speed and accuracy of the parameter adjusting process in the ‘learning phase’ of networks. Changing rules of the factor values must be defined so that both the adjusting speed and accuracy of the adjusting process is good. System operators must also decide the sizes and topologies of networks. As described above, genetic algorithms include also several critical selection issues. If single SC methods are full of different selection issues, it can be said that hybrid SC systems include even more selections.

In addition, many SC methods need considerable number of computations. The well- known characteristic of MLPs and genetic algorithms is that the convergence time of the systems in the ‘learning phase’ may be long. The number of computations increases when the strictness and complexity of systems increases. The systems may need a huge number of iterations for achieving the optimal ‘learning’ results. This is a major obstacle in using SC methods for certain time-critical tasks.

In summary, wrong factor values or wrong selections reduce the performance of systems or they can even cause malfunction of systems. The performance of SC methods depends strongly on the ‘goodness’ of these factor values and other selections, and the selections must be made individually for different tasks. In addition, the computation capacity needed for the ‘learning phase’ of some SC systems limits the use of these systems for some tasks. Because of these facts, it can be argued that SC methods are quite heuristic, difficult to manage and they may not solve all problems optimally. However, SC methods are usually used for tasks where they perform better than any traditional method. For this reason, using SC systems make sense.

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TABLE 1, part 1. Characteristics of soft-computing methods Issues Fuzzy systems Neural

networks Genetic algorithms

Hybrid systems

Principal tasks

Transferring of human knowledge about systems to computer-based control systems

Modeling of systems Classification tasks

Optimizing tasks Improving performance of single soft-computing methods by using several different soft-computing schemes for solving control tasks.

Advantages

Fuzzy systems can solve control of both highly non-linear and linear systems Easy, user-friendly management System operators can themselves decide control operations of controllers.

Feedforward neural networks can model both non-linear and linear systems. They are especially well-suited for situations where exact system models cannot be constructed. Self-organised networks (e.g. SOM) can be used for classification tasks. Generally speaking, neural networks can be used for implementing automatically and dynamically tuned control systems. Human management of control systems is not necessarily needed after the initialization phase of controllers.

Genetic algorithms are especially well-suited for various optimizing tasks of non-linear systems. Optimizing tasks can be performed without any system model. Genetic algorithms do not suffer from the well-known ‘local minimum’ problem. They always find optimal solutions.

It is possible to increase the performance of control systems using hybrid systems. Using the hybrid systems, manual tuning of some soft-computing methods (e.g. fuzzy controllers) can be avoided.

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TABLE 1, part 2. Characteristics of soft-computing methods Issues Fuzzy systems Neural

networks Genetic algorithms

Hybrid systems

Disadvantages Operators of controllers must have knowledge about the controlled system. Controllers must be retuned manually if principles of controlled systems or control targets change.

Tuning of parameters of neural networks may need a lot of computations. Neural networks are not good for extrapolation tasks. They can produce good results only in an environment which is similar to the environment used in the parameter tuning phase. Parameters of neural networks must be set correctly to achieve good performance.

Finding of optimal solutions may need a huge number of computations. Genetic algorithms also include several parameters which should be set correctly for achieving optimal results.

- If single SC methods are full of different parameter selection issues, the hybrid SC systems include even more selections. The selections must be made correctly to achieve good results.

3.5.2. Overview of soft-computing solutions for network control problems Fuzzy systems, neural networks, genetic algorithms and various hybrid systems are widely used for different network control and management tasks. For instance, they have been used for various prediction and estimation tasks (see e.g. [116]-[126]), for classifying data flows, for QoS issues (e.g. [127]-[129]), for e-commerce tasks (e.g. [130]), for traffic and network modeling (e.g. [131]-[134]), for traffic routing decisions (e.g. [135]-[141]), for fault management of networks [142], for admission control (e.g. [143]-[148]) and for data rate/congestion control tasks (e.g. [149], [150], [151], [152], [153]). The targets of soft-computing methods have varied with the development of network technology. During the 1990s, researchers concentrated on solving the control problems of ATM networks. Recently, wireless/mobile networks and IP networks are the popular targets of soft-computing methods. It could be argued that networks are full of problems which can be solved by soft-computing methods. Different methods for different problems. Use of soft-computing methods for network control follows the general principles of use of SC methods. Fuzzy systems are mainly used for problems which can be solved using the power of deduction of system experts. Data rate/congestion control (see e.g. [152]), some wep mining functions (see e.g. [154]), advertisement decisions in e-commerce (see e.g. [130]), traffic classification and traffic routing (see e.g. [155]) are good examples of these kinds of tasks. In these control tasks, events or characteristics of the controlled systems can be described using a few input

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variables. The input variables can be selected so that system experts can define logical control actions for different control situations. For instance, ‘an occupancy level of a buffer’ and ‘change of an occupancy level’ could be input variables of a congestion control system. The output variable of the controller could indicate ‘a need for data rate changes of traffic sources’. For example, if occupancy level is ‘suitable’ and change of an occupancy level is ‘zero’, then the need for data rate changes could be ‘zero’. From the perspective of a system expert, it is relatively easy to set this kind of logical control rules. The principles of fuzzy logic ensure smooth control actions, although system experts can play with unambiguous control rules. Although manual tuning of fuzzy controllers is a relatively easy task, it has been popular to design self-tuning fuzzy controllers (see e.q. [156]). There are two principal reasons for this strategy. At first, it has been argued that self-tuning fuzzy controllers could ensure more strict control operation than manually tuned ones. Carefully designed tuning algorithms do not make human errors. The second reason is the easier usability of control systems. Obviously, systems are easier to use without manual tuning tasks.

Neural networks have been mainly used for system modeling and prediction tasks. For instance, they have been used for predicting speech quality in voice over IP service [120], for predicting cell loss ratios [119], for modeling queue systems [133] and for modeling network traffic (see e.g. [157], [131], [117], [158]). Obviously, prediction results of neural networks are useful for various network control tasks, if only the results are exact enough. Flow control, routing and connection admission control are just a few example targets for which prediction and modeling results of neural networks can be used. From the network control point of view, the uses of fuzzy systems and neural networks are different. Fuzzy systems usually define what should be done in particular situations. Neural networks usually indicate what will happen in the future when the recent situation of a system is known.

A traditional task of genetic algorithms is optimization. This capability has been used also for network control problems. For example, genetic algorithms have been used for finding optimal routes for traffic (see e.g. [138]), for defining optimal sizes and shapes of cell for a cellular network [159], for implementing connection admission control for wireless and mobile networks [147][143] and for optimizing channel allocation in mobile networks [160]. For control of networks, the principal advantage of genetic algorithms and neural networks is the same. It is possible to build control systems which can perform their tasks automatically without exact information or a model of the controlled environment.

3.5.3. Use of soft-computing methods for the control tasks of this thesis The rate control of the assured service in B-ISDN networks using soft-computing methods has been a popular research item. During the 1990’s, several studies (see e.g. [161][162][151][163][149][132][156][164][116][165][166][167][168][169][170]) were published in this research area. In contrast, there are clearly fewer studies (see e.g. [171][172][173][174][175][176][164]) on the use of soft-computing methods for control tasks of DS-enabled networks. One reason for this situation may be the novelty of the DS model itself. On the other hand, the simplicity of the DS model limits the use of soft-

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computing methods. In many cases, computationally intensive soft-computing methods are not perfect solutions for control tasks of naturally simple DS-enabled networks. At least it should be considered carefully what control tasks it is attempted to solve by soft-computing approaches and where the control operations are performed. Soft-computing approaches for DS-enabled networks. Use of soft-computing methods in DS-enabled networks has mainly concentrated on controlling QoS of networks. For instance, a research work published in [164] concentrates on defining dynamic threshold levels for RED (Random Early Detection) queues by using a fuzzy controller. The authors argue that traditional fixed threshold levels are not perfect for situations where states of networks (e.g. number of sources) change dynamically. In the proposed system, the threshold levels vary dynamically according to control decisions of a developed controller. In [175], a fuzzy controller is used for implementing a leaky bucket mechanism of DS routers. The mechanism decides if traffic is in-profile or out-profile for considered AF classes. In [172], a policy-based management system is used for ensuring proper QoS for traffic using the EF service. The mechanism, including two fuzzy controllers, adjusts dynamically weights of a traffic scheduler and token bucket rate for achieving proper QoS for traffic of the EF service. In [171], the authors implement a fuzzy controller for controlling the relationship of drop and delay priorities of DS classes. From the control point of view, all these studies have two common characteristics. At first, they use very basic manually tuned fuzzy controllers for the control operations. Secondly, it is expected that the control systems are scalable, although the control actions are performed in core routers. These two characteristics indicate that the use of soft-computing methods for DS-enabled networks is a new research area. Researchers have not yet tried to optimize controllers by using e.g. hybrid soft-computing methods. Furthermore, the placing of relative complex controllers even in core routers indicates that the scalability of systems has not yet been optimized. In fact, this situation is similar to the situation of the fuzzy rate control systems for B-ISDN networks in their early days. From this point of view, it could be assumed that different auto-tuning soft-computing schemes for DS-enabled networks will become common in the future. Soft-computing solutions for rate control / congestion control of the assured service in B-ISDN networks. The soft-computing based solutions for the rate control task are mainly focused on calculation of data rates for connections using assured services in output ports of nodes. In most studies, the standard ABR rate control protocol, or some similar protocol, defines data rates for traffic sources using the calculations of nodes.

The soft-computing solutions for the rate control can be divided at least into four categories. In the first category (see e.g. [162]), simple manually tuned fuzzy controllers are used for the control task. These systems were typical, especially in the early days of soft-computing based rate control. The systems use typically two inputs: 1) the current occupancy of the output buffer for the assured service and 2) the occupancy change ratio of the buffer. Using these input variables, a controller calculates periodically factor values for output ports of nodes. These values indicate the optimal summary data rate of assured traffic proportionally to the transmission capacity of links. A Mamdani type of fuzzy interface is typically used in the systems. The understandable reason for this selection is the easy management of these fuzzy systems. It is relatively easy to design

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heuristic if–then types of rules for defining the rate control values. At least during the early days, computation of rate control values for output ports was performed in nodes. However, it was argued that the nodes do not necessarily need any fuzzy controllers (see e.g. [162]). Decision functions of fuzzy controllers can be calculated off-line in some computer, and these functions can be transferred to the nodes as numeric tables which indicate control decisions for defined situations. The numeric tables must not be transferred too often, because the control decisions of the fuzzy controllers are quite static. A typical structure of controllers of the first category is described in Fig. 7.

It has also been attempted to solve the need for relatively high computation capacity for calculations of fuzzy controllers in other ways. There is at least one study (see [151]) in which a neural network based solution is used for mimicking a fuzzy controller. The authors argue that their neural network solution needs less calculation than the use of fuzzy controllers in nodes. Obviously, researchers have also developed plenty of different chip implementations for soft-computing applications.

The controllers of the second category include a self-tuning functionality for fuzzy controllers. Self-tuning controllers are used for achieving better performance and usability of controllers compared to the manually tuned ones. At least neural networks and gradient-based methods are used for optimizing the rate control task. (see e.g. [177]). In [149], the developed system consists of a neural-fuzzy network and a fuzzy interface engine. The neural-fuzzy network manages the fuzzy interface engine, and the fuzzy interface engine calculates rate control values for sources. The authors have used a “fuzzy adaptive learning control network” (FALCON) [149] as a neural-fuzzy network.

Fig. 7. A typical structure of controllers of the first category.

An occupancy level of the buffer for the assured service.

A change in the occupancy level.

A manually tuned fuzzy controller. A factor value,

which indicates the optimal data rate for the assured traffic.

*

Capacity of a link (bit/s)

Optimal data rate for the assured traffic in an output port (bit/s)

/

A number of connections which are using the assured service.

RESULT An optimal data rate value for individual sources. (bit/s)

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From the perspective of network operators, the obvious benefit of these systems is that operators need not tune fuzzy controllers. Self-tuning systems can be designed so that the operators just set the optimizing criteria for the systems and the systems tune themselves according to the criteria. In addition, self-tuned systems can include more input variables than manually tuned ones. In this way, the accuracy of self-tuning systems is better than that of manually tuned ones, if the self-tuning controllers operate correctly. For example, the system represented in [178] includes four input variables, while manually tuned systems include usually just two or three inputs. The manually tuned systems can be managed more easily when the number of input variables is limited.

The control systems of the third category use fuzzy controllers for increasing the performance of some other rate control schemes. For instance, fuzzy controllers are used with auto regressive (AR) traffic prediction models (see e.g. [132]) and with the Erica+ congestion control algorithm (see e.g. [163]). In [132], a traffic load is predicted using the AR process and the prediction results are used for a rate control task. Fuzzy systems are used for dividing traffic into several clusters and individual AR models are used for describing the traffic of the clusters. In this way, the prediction task can be divided into several smaller and easier subtasks. In [163], a fuzzy controller is used for calculating values of one principal factor in the Erika+ algorithm. The developed system can into account of occupancies of ABR, CBR and VBR queues in the rate calculation process. Controllers of the fourth category (see e.g. [165][168]) are based on the use of neural networks. Neural network based rate control systems are not as popular as the other mentioned rate control systems. The principal idea of these systems is to predict the behaviour of high priority traffic and control data flows of the assured service using the prediction results. The systems can be designed so that they have auto-tuning capability. Operators do not need to tune the systems, but they also lose a large part of the control of the systems. In addition, it has been argued [166] that the capability of neural networks for predicting data rates of variable bit rate data streams is limited. The authors argue that prediction results of neural networks depend on similarity of traffic used in tuning and using phases of the controllers. Good results can be achieved if only statistical characteristics of traffic loads used in the phases are similar enough. 4. Developed control systems for assured services The suitability of different soft-computing based control schemes for assured services was studied. The tested soft-computing methods varied from the simple manually tuned Mamdani-type fuzzy controllers to automatically tuned ANFIS-based systems. The key questions studied in this thesis were: 1) what kinds of controllers should be used for the rate control task, 2) which components perform the control tasks and 3) how is the control information transferred between the performing components.

The rate control systems developed in this study can be separated according to the soft-computing methods used and the locations of control operations. In the early systems, the simple manually tuned Mamdani-type fuzzy controllers were used. The control actions

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were performed both on the network nodes and on user terminals. The next step was to change the performing components. In these systems, control actions were still calculated using the Mamdani-type fuzzy controllers, but the major control actions were performed on edges of networks and on user terminals. The principal reason for this strategy was to increase the data transmission capacity of the core nodes. It appeared impossible to perform time-consuming control calculations in core nodes which are optimized just for fast data transmission. The final step was to use the ANFIS neuro-fuzzy network for predicting data rates of higher priority data flows in the near future. The free capacity for the assured service was defined based on these predictions. In these studies, control operations were still performed at edge nodes of networks, but ANFIS predictors were used for predicting data rates of higher priority data flows. The principal reason for this selection was to test the suitability of a self-tuning soft-computing based controller for the control task. In addition, ANFIS-based predictors did not require manual tuning.

The network model used in the studies did not follow any existing standardized network architecture. However, the traffic control principles closely followed the basic control principles of the ABR service category and the networks used in simulations contained typical elements of connection-oriented high speed packet switched networks.

The research work done for congestion control systems is published in six publications. In these six publications, the design principles of the developed controllers vary. In the first two publications, ([P1] and [P2]), the simple manually tuned Mamdani-type fuzzy controllers were used for producing the control decisions. In [P3]-[P6], the control operations were located at edges of networks and at the user terminals. In [P4]-[P5], the ANFIS predictor was used for predicting the load of the higher priority traffic in the near future. In [P6], the performance of different rate control strategies was compared. Next the ideas behind the various control strategies are described. The publications are introduced in Sections 4.1. – 4.4. The essential results of the studies are discussed in Section 4.5.

4.1. The early study Introduction. In the early study, the designed control protocol was based on the closed control loop. Control information of the protocol flowed across the nodes of the network to the destination terminals and back to the source terminals. In this way, both the network and end-user terminals were able to participate in the control of the data rate of the assured service connections.

At the beginning of the transmission, connections started their transmission using agreed initial data rates. These data rates were negotiated with the network in the connection setup phase. During the transmission, the protocol controlled data rates of the connections so that the capacity not consumed by the high priority connections was shared optimally between the assured service connections. The control actions of the protocol were based on the control decisions of the fuzzy controllers. The nodes of the network and the destination terminals included fuzzy controllers. The fuzzy controllers of the nodes observed buffer occupancy levels of the assured service in output ports and calculated how much data rates should be decreased

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or increased for achieving the optimal occupancy level. When any data cell of the assured service flowed through an output port, the control system of the output port was allowed to copy a control value into the cell, if the control value limited data rates more than the existing control value of the cell. Destination terminals of the connections received the control opinion of a network. According to the received control information and the measurements of the destination terminals, the fuzzy controllers of the destination terminals decided if control cells were sent to the source terminals. These decisions were made periodically. The control cells were sent from the destination terminal to the source terminal using the same route as the data cells of connections, but in the reverse direction. Along routes of control cells, nodes again compared their own control value with the control values of control cells. If control values of nodes limited the speed of connections more than the existing control values of the control cells, the nodes had permission to copy their own control values into the cells. The source terminal adapted the data transmission rates according to the control values of received control cells. Controller of nodes. The principal task of the fuzzy controllers of the nodes was to control the occupancy levels of the buffers. The fuzzy controllers had two input variables: 1) the buffer occupancy level of the node and 2) the predicted change in the occupancy level of the buffer. The second input variable indicated how much the occupancy level of the buffer would change during the defined time. The output variable of the fuzzy controllers indicated how much data rates should have been increased or decreased. The first input variable was defined by seven linguistic values and the second input variable by nine linguistic values. The output variable was defined by seven linguistic values and the numerical range of the variable was [-1,1]. Negative values of the output variable indicated the need to decrease data rates because of the danger of overflow of the buffer. Correspondingly, positive values indicated the need to increase data rates for achieving better use of the data transmission resources. Calculation of numeric values of the variables is defined in Equations 1-2.

As described in [P1], the fuzzy controllers of the nodes included three different rulebases. They were used according to the variation in the occupancy level of the buffers of the assured service. These three fuzzy rulebases were tuned for situations of increasing, decreasing and stable occupancy levels of the buffers. The division was made to achieve easier use of the control system. It was thought that manual tuning of three different databases with two input variables is easier than the tuning of one large rulebase with three input variables.

The rulebases of the controllers of the nodes were tuned so that the controllers tried to keep the occupancy levels of the buffers at the optimal level, regardless of variation in the data rates of higher priority connections. Table 2 includes an example of the rulebases.

Current occupancy = # of ABR data cells in buffer (cell)

Optimal # of data cells in buffer (cell) (1)

Occupancy change = (Pred. total load - Capacity) * Obs. time

Optimal # of data cells in buffer (2)

Obs.time = Observation time, which is RTT≤

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Controller of destination user terminals. The fuzzy controllers of the destination terminals included two input variables. These variables were 1) the network control factor and 2) the amount of received data. The network control factor defined how much the rata rate of the connection should be changed in proportion to the maximum data rate change factor value of the network. The amount of received data was a relative value which indicated how much data a destination terminal had received during the measurement time related to the amount of data that would have been received using the initial data rate of the connection. The first input variable was equal to the fuzzy output variable of the fuzzy controllers operating in the nodes. The second input variable was defined by five linguistic values. Calculation of numeric values of the input variables is defined in Equations 3-4.

The output variable of the fuzzy controller indicated whether it was necessary to send the special control cell to a source terminal. The variable was defined by two linguistic values and the numerical range of the variable was [0,1]. Numerical values smaller than 0.5 indicated the necessity of sending the control cell.

The controllers of destination terminals co-operated with the controllers of the nodes. Connections could use at least the nominal bit rate and it was attempted to avoid overflow situations in the networks. A rulebase of the controller is described in Table 3.

Linear controllers of source terminals. The simple task of the linear controllers of the source terminal was to adjust data rates of the assured service connection according to the received instructions. No logical control operations were needed in the source terminals because the controllers of nodes considered the status of the output buffers and the controllers of the destination terminals considered the benefits of the connections. The adjustments of data rates were done according to Eq. 5.

Input1 = The network control factor (3)

Amount of received data = Received data

Received data using initial rate (4)

DR(n +1) = DR(n) + NCF(n) * MaxDRCF(n) (5)

DR = Data rateNCF = Network control factor (sent by the destination terminals) : [-1,1]MaxDRCF = Maximum data rate change factor. Value of the factor is define by the node, which is allowed to copy the control value into the control cell.

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Table 2, part 1: Rulebases of the fuzzy controllers on the network nodes Rulebase Rules For increasing occupancy levels

IF Current occupancy=little AND Occupancy change=neg.large THEN control=increase rate heavily IF Current occupancy=little AND Occupancy change=neg.small THEN control=increase rate carefully IF Current occupancy=little AND Occupancy change=stable THEN control=do not change data rate IF Current occupancy=little AND Occupancy change=pos.small THEN control=do not change data rate IF Current occupancy=little AND Occupancy change=pos.large THEN control=do not change data rate IF Current occupancy=middle AND Occupancy change=neg.large THEN control=Decrease rate carefully IF Current occupancy=middle AND Occupancy change=neg.small THEN control=Decrease rate carefully IF Current occupancy=middle AND Occupancy change=stable THEN control=Decrease rate carefully IF Current occupancy=middle AND Occupancy change=pos.small THEN control=Decrease rate heavily IF Current occupancy=middle AND Occupancy change=pos.large THEN control=Decrease rate heavily IF Current occupancy=ok AND Occupancy change=neg.large THEN control=Decrease rate carefully IF Current occupancy=ok AND Occupancy change=neg.small THEN control=Decrease rate heavily IF Current occupancy=ok AND Occupancy change=stable THEN control=Decrease rate heavily IF Current occupancy=ok AND Occupancy change=pos.small THEN control=Decrease rate heavily IF Current occupancy=ok AND Occupancy change=pos.large THEN control=Decrease rate heavily IF Current occupancy=large AND Occupancy change=neg.large THEN control=Decrease rate heavily IF Current occupancy=large AND Occupancy change=neg.small THEN control=Decrease rate heavily IF Current occupancy=large AND Occupancy change=stable THEN control=Decrease rate heavily IF Current occupancy=large AND Occupancy change=pos.small THEN control=Decrease rate heavily IF Current occupancy=large AND Occupancy change=pos.large THEN control=Decrease rate heavily IF Current occupancy=huge AND Occupancy change=neg.large THEN control=Decrease rate heavily IF Current occupancy=huge AND Occupancy change=neg.small THEN control=Decrease rate heavily IF Current occupancy=huge AND Occupancy change=stable THEN control=Decrease rate heavily IF Current occupancy=huge AND Occupancy change=pos.small THEN control=Decrease rate heavily IF Current occupancy=huge AND Occupancy change=pos.large THEN control=Decrease rate heavily

Table 2, part 2: Rulebases of the fuzzy controllers on the network nodes Rulebase Rules For stable occupancy levels

IF Current occupancy = little AND Occupancy change = neg.large THEN Control = Increase rate heavily IF Current occupancy = little AND Occupancy change = neg.small THEN Control = Increase rate heavily IF Current occupancy = little AND Occupancy change = stable THEN Control = Increase rate heavily IF Current occupancy = little AND Occupancy change = pos.small THEN Control = Increase rate heavily IF Current occupancy = little AND Occupancy change = pos.large THEN Control = Increase rate heavily IF Current occupancy = middle AND Occupancy change = neg.large THEN Control = Increase rate carefully IF Current occupancy = middle AND Occupancy change = neg.small THEN Control = Increase rate carefully IF Current occupancy = middle AND Occupancy change = stable THEN Control = Increase rate carefully IF Current occupancy = middle AND Occupancy change = pos.small THEN Control = Increase rate carefully IF Current occupancy = middle AND Occupancy change = pos.large THEN Control = Increase rate carefully IF Current occupancy = ok AND Occupancy change = neg.large THEN Control = do not change data rate IF Current occupancy = ok AND Occupancy change = neg.small THEN Control = do not change data rate IF Current occupancy = ok AND Occupancy change = stable THEN Control = do not change data rate IF Current occupancy = ok AND Occupancy change = pos.small THEN Control = do not change data rate IF Current occupancy = ok AND Occupancy change = pos.large THEN Control = do not change data rate IF Current occupancy = large AND Occupancy change = neg.large THEN Control = do not change data rate IF Current occupancy = large AND Occupancy change = neg.small THEN Control = do not change data rate IF Current occupancy = large AND Occupancy change = stable THEN Control = do not change data rate IF Current occupancy = large AND Occupancy change = pos.small THEN Control = do not change data rate IF Current occupancy = large AND Occupancy change = pos.large THEN Control = do not change data rate IF Current occupancy = huge AND Occupancy change = neg.large THEN Control = Decrease rate carefully IF Current occupancy = huge AND Occupancy change = neg.small THEN Control = Decrease rate carefully IF Current occupancy = huge AND Occupancy change = stable THEN Control = Decrease rate carefully IF Current occupancy = huge AND Occupancy change = pos.small THEN Control = Decrease rate carefully IF Current occupancy = huge AND Occupancy change = pos.large THEN Control = Decrease rate carefully

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Table 2, part 3: Rulebases of the fuzzy controllers on the network nodes Rulebase Rules For decreasing occupancy levels

IF Current occupancy = little AND Occupancy change = neg.large THEN Control = Increase rate heavily IF Current occupancy = little AND Occupancy change = neg.small THEN Control = Increase rate heavily IF Current occupancy = little AND Occupancy change = stable THEN Control = Increase rate heavily IF Current occupancy = little AND Occupancy change = pos.small THEN Control = Increase rate heavily IF Current occupancy = little AND Occupancy change = pos.large THEN Control = Increase rate heavily IF Current occupancy = middle AND Occupancy change = neg.large THEN Control = Increase rate heavily IF Current occupancy = middle AND Occupancy change = neg.small THEN Control = Increase rate heavily IF Current occupancy = middle AND Occupancy change = stable THEN Control = Increase rate heavily IF Current occupancy = middle AND Occupancy change = pos.small THEN Control = Increase rate heavily IF Current occupancy = middle AND Occupancy change = pos.large THEN Control = Increase rate heavily IF Current occupancy = ok AND Occupancy change = neg.large THEN Control = Increase rate heavily IF Current occupancy = ok AND Occupancy change = neg.small THEN Control = Increase rate heavily IF Current occupancy = ok AND Occupancy change = stable THEN Control = Increase rate heavily IF Current occupancy = ok AND Occupancy change = pos.small THEN Control = Increase rate heavily IF Current occupancy = ok AND Occupancy change = pos.large THEN Control = Increase rate heavily IF Current occupancy = large AND Occupancy change = neg.large THEN Control = Increase rate heavily IF Current occupancy = large AND Occupancy change = neg.small THEN Control = Increase rate heavily IF Current occupancy = large AND Occupancy change = stable THEN Control = Increase rate heavily IF Current occupancy = large AND Occupancy change = pos.small THEN Control = Increase rate heavily IF Current occupancy = large AND Occupancy change = pos.large THEN Control = Increase rate heavily IF Current occupancy = huge AND Occupancy change = neg.large THEN Control = do not change data rate IF Current occupancy = huge AND Occupancy change = neg.small THEN Control = do not change data rate IF Current occupancy = huge AND Occupancy change = stable THEN Control = do not change data rate IF Current occupancy = huge AND Occupancy change = pos.small THEN Control = do not change data rate IF Current occupancy = huge AND Occupancy change = pos.large THEN Control = do not change data rate

Table 3: Rulebases of the fuzzy controllers on the receiver terminals Rulebase Rules IF Control = increase rate heavily AND Amount of data = very low THEN Send control cell = yes

IF Control = increase rate heavily AND Amount of data = low THEN Send control cell = yes IF Control = increase rate heavily AND Amount of data = ok THEN Send control cell = yes IF Control = increase rate heavily AND Amount of data = high THEN Send control cell = yes IF Control = increase rate heavily AND Amount of data = very high THEN Send control cell = yes IF Control = increase rate carefully AND Amount of data = very low THEN Send control cell = yes IF Control = increase rate carefully AND Amount of data = low THEN Send control cell = yes IF Control = increase rate carefully AND Amount of data = ok THEN Send control cell = yes IF Control = increase rate carefully AND Amount of data = high THEN Send control cell = yes IF Control = increase rate carefully AND Amount of data = very high THEN Send control cell = yes IF Control = do not change data rate AND Amount of data = very low THEN Send control cell = yes IF Control = do not change data rate AND Amount of data = low THEN Send control cell = not IF Control = do not change data rate AND Amount of data = ok THEN Send control cell = not IF Control = do not change data rate AND Amount of data = high THEN Send control cell = not IF Control = do not change data rate AND Amount of data = very high THEN Send control cell = not IF Control = decrease rate carefully AND Amount of data = very low THEN Send control cell = not IF Control = decrease rate carefully AND Amount of data = low THEN Send control cell = not IF Control = decrease rate carefully AND Amount of data = ok THEN Send control cell = not IF Control = decrease rate carefully AND Amount of data = high THEN Send control cell = yes IF Control = decrease rate carefully AND Amount of data = very high THEN Send control cell = yes IF Control = decrease rate heavily AND Amount of data = very low THEN Send control cell = not IF Control = decrease rate heavily AND Amount of data = low THEN Send control cell = not IF Control = decrease rate heavily AND Amount of data = ok THEN Send control cell = not IF Control = decrease rate heavily AND Amount of data = high THEN Send control cell = yes IF Control = decrease rate heavily AND Amount of data = very high THEN Send control cell = yes

4.2. Moving control operations to edges of networks The next development step was to move the most complex control operations of core nodes to edge nodes. The second change was to use several alternative paths between the

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edge nodes for transferring data of the assured service. Thirdly, the system included two alternative strategies for adjusting the data rates; the explicit and the iterative adjustment strategies. In this way, control operations were more complex than in the first studies, but the edge nodes had also more computation capability than the core nodes. The layout of the system of the second development step is described in Fig. 8, and the details of the system are described in [P3].

The control system of edge nodes included three basic control functions. The control functions were: 1) The collection of the network control information of the backbone network, 2) selection of the optimal path and 3) data rate adjustment of the assured service connections. The control related tasks of the core nodes were to 1) transfer the control information between the edge nodes and 2) measure their own control information. The control operations were done periodically for every traffic flow of the assured service. During each control moment, the control systems of the edge nodes decided which paths were used to transmit assured traffic through the backbone network and how the data rates of assured traffic flows were adjusted for optimizing the load of the nodes in the chosen paths. Between the control moments, the source terminals sent data using the previously adjusted data rates and selected paths.

Fig. 8. Layout of the control system in the second development step.

The network load information of the backbone network was collected periodically. Edge nodes sent special ERM (Edge Resource Management) packets to the backbone network. The backbone network broadcast ERM packets through the core nodes to all other edge nodes. The control information of the core nodes was saved to the ERM

Access networks with source terminals

Access networks with destination terminals.

Edge nodes included all the most time-consuming control functions. They also sent periodically the special ERM packets to a network to collect load information of core nodes.

For the control, core nodes just measured the load of their output ports and transferred the ERM packets.

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packets according to the max-min fairness principle. In this way, the core node which had the highest load in a path was allowed to represent the load of the path. After receiving any ERM packet, the edge nodes updated the control information related to the path of the received ERM packet to a special database. This database included the control information of all alternative paths between the edge node and all other edge nodes of the backbone network. The control operations of the edge nodes were done according to the information of this database.

The data rates of the assured service connections were adjusted explicitly or iteratively, depending on the load situation of the network. In the explicit data rate adjustment strategy, the source terminals adjusted their data rates directly to the defined data rate. The iterative data rate adjustment changed the data rates by small steps. It was assumed that the network applications adapt more easily to small data rate changes than to large ones. Hence, explicit data rate changes were used only when they were urgently needed to avoid congestion in the network or to adapt to high load changes in the high priority traffic.

4.3. Prediction of the data rates of the high priority data flows Background. In [P1] and [P2], variation of the data rates of the high priority data flows was considered during the adjusting process of data rates of the assured service. The strength of data rate adjustments depended on the strength of the data rate variations of high priority data flows (see Eq. 5). In Eq. 5, the value of the ‘MaxDRCF’ factor varies according to the strength of a variation of the data rates of the high priority data flows, as described in [P2].

In [P3], the data rates of the assured data flows were adjusted explicitly or iteratively. In the iterative case, prediction of the data rates of the high priority data flows was done similarly as in the systems of the first development step. In the explicit case, data rates of the high priority data flows were predicted using the simple linear scheme. The prediction was based on two consecutively measured summary data rates of the high priority data flows, as described in Fig. 9. The measurements indicated changing direction and speed of high priority traffic. Using this information, it was possible to predict the load of the high priority traffic after a measurement interval. The examples of Fig. 9 clearly indicate the principal weakness of the simple linear prediction. The prediction results may be very imprecise, if the time between the consecutive measurements is too long in relation to the variation in the summary data rate. From the other perspective, if the prediction time is very short, the whole prediction task is quite unnecessary.

It is very difficult, if not impossible, to predict real data traffic in the network. As concluded in [166], the exact statistical characteristics of the predicted traffic should be known to produce good predictions. In real network environment, it is even impossible to collect the data fast enough to produce the exact statistical model of the predicted traffic. Hence, it is very important to consider what kinds of traffic flows it is attempted to predict, how exactly predictions should be made and what methods are used for predictions.

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Fig. 9. A problem of too simple prediction.

Traffic prediction can be done using several prediction methods. However, a common characteristic of almost all prediction methods is to use the time series for the prediction. The next value or values of a certain characteristic of the traffic flows are predicted using the measured past characteristics of the traffic flows. During the past few years, several studies have been published in which the behaviour of the VBR (Variable Bit Rate) type of traffic has been predicted using LMS (Least Mean Square), RLS (Recursive Least Square) or fuzzy logic based methods (see e.g. [179] [180] [181] [126] ). The congestion control system of the third development step. The next development step in the research work was to study more advanced methods for predicting data rates of high priority data flows in the near future. An ANFIS [90] predictor was used for the predictions. The reason for this selection was that ANFIS has been found to be effective in modelling the behaviour of highly non-linear dynamic systems [182]. Secondly, the parameter adjusting process of ANFIS networks requires notably less computation than MLP (Multi-Layer Perceptron) networks, which have been commonly used for similar tasks as ANFIS networks. Thirdly, it was possible to implement a self-tuning controller using ANFIS.

The use of ANFIS for the prediction task is presented in [P4] and [P5]. In the system of [P4], the data rate adjusting system followed the principal of the system of the second development step. It still included both explicit and iterative data rate adjustment schemes, but data rates of the high priority data flows were predicted using ANFIS, instead of the simple prediction scheme, introduced in Fig. 9.

In the system of [P5], only the explicit data rate adjustment scheme was used. Data rates of the high priority data flows were predicted purely using ANFIS. The primary aim of the study of [P5] was to test the performance of ANFIS in the prediction task. The system included several timers, which affected the collection of the control information

Data rate

Time

Summary data rate of high priority data flows

Case 1

Case 2

tm1 tm1

tm2

50

from core nodes, and the control frequencies of the edge nodes. The effects of different values of these timers on the performance of the system were tested. A well-known fact is that the size of the ANFIS network affects the performance of the network. For this reason, the effects of different sizes of ANFIS networks on the performance were also tested.

Variables of the ANFIS predictors were equal in both studies of [P4] and [P5]. Three input variables had been chosen to describe the dynamic characteristics of the high priority data flows. These three variables were 1) the maximum of the data rate values of the high priority data flows during the measurement time, 2) the mean of the data rate values of the high priority data flows during the measurement time and 3) the difference between the last two data rate measurements of the high priority data flows during the measurement time. The output value of the ANFIS predictor was the maximum predicted total data rate of the high priority data flows in a route. Use of the maximum data rate value was chosen because it prevented overflow situations in nodes the most efficiently. Fig. 10. Construction of input-output data pairs.

Before the ANFIS predictor could be used, its parameters had to be tuned for a prediction case. The parameters were always set in the off-line mode. At first, the statistically representative sample values of the data rates of the high priority data flows were collected in the routes between the edge nodes. After that, the input-output data pairs were constructed and the parameters of the ANFIS predictors were set by the special hybrid learning algorithm [105] of the ANFIS network. As a result, the ANFIS predictors could predict the data rates of the high priority data flows in the near future. The construction of input-output data pairs is presented in Fig. 10.

Used for calculation of value of output variable

(n) sequential data rate measurements

Used for calculation of values of input variables Data rate

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4.4. Comparison of different rate control schemes Different data rate control schemes for the assured service are compared in [P6]. The main objective of this publication was to compare the technical performance of the congestion control strategies with respect to their efficiency in the sense of the required computation. From this point of view, we studied what kind of congestion control strategy would have been optimal to achieve the general aims of the assured service.

The network environment of the study was similar to that used in the studies of [P3] – [P5]. Time-consuming control operations were distributed to the edge nodes of the network, and the core nodes just measured their buffer occupancy levels and transferred the special control packets. The major difference between the study of [P6] and the studies of [P3]-[P5] was that the control systems in [P6] did not select paths for the data flows dynamically. Constant paths were used for the data flows during transmissions. This selection was done for ensuring easier comparison of the performance of the tested control methods.

Four congestion control methods were chosen for the comparison study. One of them was based on both the occupancy information of the output buffers and the load changes of the high priority data flows. The other three were based on different load prediction methods of the high priority data flows. The methods studied were the following:

1) Congestion control method based on the observed data rate values of

the high priority data flows. This congestion control method was the simplest of all tested congestion control methods. It was purely based on the measured data rates of the high priority data flows. The principal idea of the method was that the ‘predicted’ loads of the high priority data flows in the near future were expected to be the same as the recently measured loads.

2) Congestion control method based on statistical information of high

priority flows and random values. The main idea of this method was to produce randomized values of the data rates of the high priority data flows, which roughly followed the measured statistical characteristics of the high priority data flows. These values fulfilled the role of the predicted data rate values of the high priority data flows in the near future. The data rates for the data flows of the assured service were defined using these randomized data rate values of high priority data flows.

3) Congestion control method based on non-linear predictions of the

data rates of the high priority data flows. As in the first two congestion control methods, the calculation of the data rates for the controlled data flows was based on the prediction of the data rates of the high priority data flows. The difference between this and the first two methods was the different complexity of the data rate prediction functions. In the first two congestion control methods, the prediction

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functions were designed to be as simple as possible in order to reduce the amount of computations for the predictions. In contrast, a relatively complex ANFIS network was used for the prediction task. The method tried to predict the data rates of the high priority data flows as accurately as possible. The accurate prediction algorithm was expected to ensure better performance of the congestion control method than simpler ones. The price which had to be paid was the huge amount of computations compared to simpler prediction algorithms.

4) Congestion control method based on both occupancy information of buffers and load of high priority data flows. The previously introduced congestion control methods were purely based on the prediction of the high priority data flows and the number of active controlled data flows. An important difference between this fourth tested method and the previous methods was that the fourth method also considered occupancy levels of buffers of the assured service in the control operations. In fact, the main aim of this method was to ensure that the occupancies of the most heavily occupied buffers of the assured service were most of the time at an acceptable level.

4.5. The summarized results of the studies It can be said that test results of all studies corresponded with the expectations. Regardless of the types of the controllers, the developed controllers were able to control the data flows of the assured service satisfactorily. Near optimal data rate and packet loss values were achieved. Various parameters of the controllers made the controllers scalable for different network environments. However, the performance of the controllers depended also on the tuning of the control systems and the characteristics of the network environments.

4.5.1. Manually tuned fuzzy controllers In the systems which used manually tuned fuzzy controllers, tuning of the controllers was a critical task. The controllers did not achieve optimal control results if the fuzzy variables or fuzzy rulebases were tuned negligently. However, these systems were also quite robust. Satisfactory control results were achieved although the settings of the controllers were not precisely optimal. The obvious reasons for this phenomenon were the iterative working of the control systems and the dynamically changing data rates of the high priority traffic. It was more important to produce logically correct rather than numerically exact control decisions. During the tests, it was clearly noticed that the Mamdani-type fuzzy controllers were suitable for producing logically correct control actions.

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It was also noticed that the number of input variables and linguistic values of the input variables were the critical design issues for usability of the fuzzy controllers. During the simulation tests of this thesis, the fuzzy controllers with two input variables and a maximum of nine linguistic values per input variable were empirically found to be the most usable for the control task. In this way, tuning of the rulebases of the controllers was a relatively easy and fast task. If the number of input variables or linguistic values had been larger, tuning of the rulebases of the controllers would have been a much more time-consuming task. The rulebases would have included many more rules and designing of a balanced set of rules would have been a more difficult task. From this perspective, it could be argued that manually tuned fuzzy controllers require a good user interface to be used effectively. The system experts must be capable of seeing how different tuning selections affect the control decisions of the controllers.

4.5.2. Characteristics of the network environment It is a well-known fact that the size of networks and the buffer capacity of nodes affect the performance of the rate control systems of the assured service. The time needed for transferring the control instructions from core nodes to source terminals increases as a function of the geographical size of networks. Control systems react to the load changes in high priority traffic more slowly in large networks than in small ones. For this reason, it can be expected that the performance of rate control systems decreases as a function of the geographical size of networks. Nodes of large networks would require also large buffers for compensating bad effects of delays. Larger buffers would give more time to control systems before overload or underload situations occur in the buffers. Obviously, the other solution is to use rate control systems which can predict the load of the network in the near future. If the predictions are at least near correct, they can help the control systems to adjust the data rates to at least near the correct value, regardless of the delays.

The characteristics of network environments affected also the performance of the control systems of this thesis. The effects are reported e.g. in [P3]. The results of [P3] are also summarized in Fig. 11. The figure indicates how good average throughput values were achieved using different buffer sizes and distances between the nodes. The throughput was measured for nine different combinations of buffer sizes and distances between the nodes. The combinations were (buffer size in cells, distance (km)): (400,300), (400,1000), (400,10000), (800,300), (800,1000), (800, 10000), (2000,300), (2000,1000) and (2000,10000). The average throughput indicates how optimally the available capacity for the traffic of the assured service was used.

According to the results, the performance of the rate control system decreased when the distance between the nodes increased or the size of the buffers decreased. The best performance was achieved when the distance between nodes was smallest and the size of the buffers was largest. However, the performance difference between the best and the worst result was only 17 %, although the buffer sizes and distances between the nodes varied greatly. Packet losses occurred only in the test case, where the buffer size was the minimum and the distance between the nodes was the maximum: case (400, 10000).

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Fig. 11. Performance in different network environments.

4.5.3. Significance of the parameters of the controllers In addition to the characteristics of networks, the parameters of the control systems affected the performance. The effects of the parameter values are discussed in [P5] and [P6]. In [P5], the operation of the ANFIS-based rate control system is tested using different parameter sets. In the study, the parameter values affected three issues: 1) how frequently the system collected the load information of the core nodes, 2) the time difference between the sequential load measurements used for ANFIS and 3) how frequently the data rates are adjusted. According to the test results, the mean throughput decreased when the values of the interval parameters increased. However, the difference between the throughput results of the best and worst tests was only 19 %, although the values of the interval parameters varied greatly. At same time, the difference between the prediction errors and the packet loss ratios of different tests was small. Obviously, the average and standard deviation values of the buffers increased, when the values of the interval parameters increased. In these cases, the sources were controlled more slowly and the buffer occupancy levels varied greatly, before the control operations affected the occupancies of the buffers. The test results are summarized in Table 2 of [P5].

4.5.4. Performance of different controllers In [P6], the effects of the parameter values are tested using different types of rate controllers. In the tests, the length of the buffers for the assured service was infinite,

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whereas the length of the buffers was finite in the other studies. This strategy made it possible to observe the buffer capacity requirements of different rate control systems.

According to the tests of [P6], it could be said that the complexity of the congestion control systems increases the performance of the methods compared to the simpler methods, but the management of the complex methods may be much more sensitive than the management of the simple methods. However, the test results indicate that successful management of the complex methods is possible.

The test results of [P6] are summarized in the figures 12, 13 and 14. The figure 12 indicates the average occupancy levels of the three most heavily loaded buffers reserved for the traffic of the assured service. The figure 13 indicates how optimally available resources for the traffic of the assured service were used. The value one in the figure indicates the optimal use of the available resources. Furthermore, the figure 14 indicates the required arithmetic operations per second and per controlled route when the tested controllers adjusted the data rates of the connections. The performance of the controllers was tested using three different control frequencies of the traffic sources. The time differences between sequential rate adjustments were 0.05, 1.2 and 3.0 seconds.

Throughput results of all controllers were approximately equal, but the advanced controllers achieved good throughput results with a notably smaller need for buffer space than the two simpler ones. Especially the controller based on considering the buffer occupancies and the load of the high priority traffic achieved a good throughput result with a very small need for the buffer space. This result was obvious because the controller tried to optimize both the throughput and the use of buffer space. However, the throughput results of the controller decreased heavily when the control frequency decreased. This result was also obvious because the controller tried to prevent overloading of the buffers. The principal weakness of the advanced controllers was that they required many more arithmetic operations for calculating control decisions than the simple ones.

Fig. 12. Average buffer occupancy levels.

Avg. buffer occupancy levels

0500

100015002000250030003500

Observeddata ratevalues

Statisticalinformation

Non-linearprediction

Buffer anddata rate info.

Test cases

Occ

up

ancy Avg 0.05s

Avg 1.2sAvg 3.0s

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Fig. 13. Average throughput values.

Fig. 14. Arithmetic operations per second per controlled route.

4.5.5. What kind of controller should be selected? At least from an academic perspective it can be discussed what kinds of controllers would be optimal for adjusting data rates of the data flows of the assured services. The issue can be divided into two subjects: 1) what information should be used for the control operations and 2) what types of controllers should be used. According to the test results

Avg. throughput values

0.000.200.400.600.801.001.20

Observeddata ratevalues

Statisticalinformation

Non-linearprediction

Buffer anddata rate info.

Test cases

Th

rou

gh

pu

t

Avg 0.05sAvg 1.2sAvg 3.0s

Arithmetic operations per second per controlled route

0.1

1

10

100

1000

10000

Observeddata ratevalues

Statisticalinformation

Non-linearprediction

Buffer anddata rate

info.Test cases

#

Avg 0.05sAvg 1.2sAvg 3.0s

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of the publications of this thesis, it can be argued that the optimal controller should consider both the occupancies of the buffers of the assured service and the available data transmission capacities of networks. As discussed earlier, the controller should also be able to predict load situations in the network at least in the near future.

A controller can also work using just the buffer occupancy information or the capacity information, but the level of performance of this kind of controller is lower than that of a controller with the two input variables. For example, the ANFIS-based controllers in [P4] – [P6] used just the capacity information. The problem with this scheme is that the controller cannot consider the buffer occupancy levels. In order to prevent packet losses, a controller should either adjust the data rates of the assured data flows according to the maximum load of high priority flows or predict the load of high priority flows exactly. Using the first option, packet losses can be avoided, but the available data transmission resources cannot be used optimally, especially if the control frequency is low (see results of [P5]). On the other hand, the exact prediction of the load of high priority is almost impossible. The test results of [P6] indicate that the ANFIS-based rate control system required notably more buffer capacity than the congestion control method based on both occupancy information of buffers and the load of high priority data flows. The obvious reason for this result is the prediction errors of ANFIS.

The other simple option would be to perform the control using just recent occupancy information of buffers. In fact, TCP uses implicitly this principle. Control of TCP flows depends on data packet losses in networks. However, pure occupancy values do not indicate how the loads of the output ports are changing and how the occupancies of the buffers are changing. If the recent buffer occupancy levels are only used for the control, the control results may be very imprecise. The controller should measure both the recent occupancy levels and the changes in the occupancy levels for achieving optimal control results. However, changes in the occupancy levels indicate the load changes of the output ports. From this perspective, it could be proposed to use the predicted available capacity in the near future as an input variable instead of the changes in the occupancy levels.

The type of controller is the second important design issue. The controller should be capable of considering at least two input variables, it should be able to consider the future load of networks and it should be easy to use. According to the test results, it could be said that both the Mamdani-type fuzzy controller and the controllers (in [P3]-[P6]) based on the ANFIS predictor were suitable for the control task. The principal advantage of the fuzzy controller is simplicity. Tuning of the controller is relatively easy and the tuning process can be made off-line. For operators, it is relatively easy to define input variables so that they give predicted information about the load of a network. After the tuning phase, the controllers are immediately ready for use. The fuzzy controllers can produce highly non-linear control decisions according to the heuristical control knowledge of the operators. Network components do not necessarily need any fuzzy controllers. The control decision functions can be transferred to network components as multi-dimensional arrays which indicate the control decisions in defined situations. It could be said that the only harmful characteristic of the manually tuned Mamdani-type fuzzy controllers is that controllers must be really tuned manually. From this perspective, the willingness of system operators to tune the fuzzy controllers is a critical issue.

Another possible scheme is to use automatically tuning controllers. The most suitable soft-computing methods for the scheme are feedforward neural networks and neuro-fuzzy

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algorithms. These soft-computing methods can be used for modelling different occurrences in networks and the models can be used for the control task. For example, ANFIS (or MLP network) can be used for predicting the load of the high priority traffic or the buffer occupancies, and the output values of ANFIS can be used for calculating the available capacity of a network for traffic of the assured service. The advantage of neural network and neuro-fuzzy systems is that the control systems do not require manual tuning. The parameters of control systems are tuned automatically using the collected input-output value pairs. The principal weakness of these methods is that the quality of the results depends on the quality of the parameter tuning process. The input-output value pairs used for parameter tuning must represent well the real control situations. If control situations are very complex, adaptation of the methods may take a long time. The methods are not the best tools for extrapolation tasks. To prevent the use of very incorrect output values for the rate control, the automatically tuning systems should include some kind of checking systems. Finally, it could be argued that satisfactory control results can be achieved more easily using the very basic manually tuned fuzzy controllers than the automatically tuning methods. It could be even said that the best characteristic of the fuzzy controller based systems is the use of human knowledge for the control decisions. In the control task, where the control is based on just a few input variables, the use of the manually tuned fuzzy controllers is more effective than any kind of automatically tuning system. However, carefully designed automatically tuning systems also produce satisfactory results, as shown by the tests results of [P3] – [P6].

5. Price setting and service selection problems of the ISPs The ISPs are responsible to their customers for ensuring that the offered end-to-end data transmission services correspond to the quality of service definitions of established SLAs at least at some statistical level. However, the estimation of the quality of end-to-end data transmission services is a complex task if data packets are routed through IP domains of several network operators. First, the DS model does not provide any kind of data flow-based control or resource reservation functions through all networks used for transferring data packets. Second, network operators are not willing to share information about resources and loads of their networks with other operators [12]. On the other hand, the operators should consider that their financial aims would be fulfilled. In fact, it could be argued that most operators would like to maximize their financial profit.

The principal contribution of this second research subject of the thesis was to develop management systems for SLAs which are able to consider the opinions of customers regarding suitable quality and the price of services. The systems adjusted quality and the prices of services according to behaviour or feedback of the customers. The systems were designed for an open and dynamic market environment in which the customers are able to select the SLAs freely, even for individual data flows. The dynamic market environment studied is not the current reality. The customers are certainly able to select their ISPs, but the agreements are quite long-term. The real market environment is not as dynamic as the environment studied in the present thesis. It also made sense to study how suitable the soft-computing methods are for this kind of decision task where people’s opinions should be dynamically considered.

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The developed management systems were designed for a network environment in which several interconnected DS-enabled IP networks offered multiple data transmission services. The terminals of the customers were connected to the IP network area via access networks. The access networks were broadband networks which were able to transfer IP packets. Data flows of the customers flowed through the (source) access networks, IP domains of the ISPs of the customers, backbone networks, IP domains of ISPs of the receivers and (destination) access networks to the receiving terminals. The ISPs defined several alternative service level agreements for both real-time and non-real-time data transmissions. Furthermore, the ISPs offered both dynamic and static service level agreements. In these studies, we concentrated on the dynamic agreements. These agreements were defined by the principal quality of service factors and a price. The price of the SLA defined the cost to customers per data unit if they established an agreement with ISPs. From this point of view, it was assumed that the senders of the data flows were paying for their data transmissions on a per-packet or per-byte basis.

The ISPs advertised their SLAs for customers. For this operation we assumed that the source terminals have some mechanism for requesting information about the SLAs of the ISPs or that the ISPs can dynamically send this information to the source terminals. In this way, the customers would always have information about the SLAs of the competing ISPs. They would know the prices of the SLAs and the advertised quality of end-to-end data transmission services which should be achieved using the SLAs. At the beginning of transmission of data flows, the customers selected the suitable SLAs for the data transmissions from the group of advertised SLAs. The terminals of the customers informed the ISPs about the SLA selections by copying an index of the selected SLAs onto the DS label of transferred data packets.

Every ingress router of the IP domains of the ISPs included a management system for defining the selling prices and the DS classes of the defined SLAs. The ingress routers did not contain any admission control functions for the dynamic SLAs studied. Data flows of all customers who were willing to accept some of the offered service level agreements were transferred. For control and billing operations, it was assumed that the ingress routers stored information about the SLA selections of the customers and the amount of transferred data. The environment of the developed management systems is described in Fig. 15.

The developed systems are published in [P7] – [P9]. In [P7], the suitability of an automatically tuning controller and a manually tuned fuzzy controller was compared. In the control systems of [P8] and [P9], the manually tuned Mamdani-type fuzzy controllers were used for producing control decisions. The difference between the studies of [P8] and [P9] was that the systems used different strategies for measuring opinions of the customers. In the study of [P8], source terminals of customers did not send any opinions regarding the offered data transmission services to the ISPs, while the system of [P9] expected a response from the customers regarding the control operations. Next, the systems are described.

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Fig. 15. The environment of the studied management system.

5.1. Comparison of different soft-computing tools for the control task We studied the usability of two different soft-computing based control systems for DS class selection and price-adjusting tasks of the ISPs. The first control system was based on the use of the manually tuned Mamdani-type fuzzy controllers, while the second system used SOM [89] and genetic algorithms (GA) [90] for implementing an automatically tuning controller for the control tasks. The fuzzy controller based control system was designed so that the minimum amount of information is used for control actions, while the automatically tuning controller used several input variables. From this point of view, we compared the performance of simple and relatively complex controllers in the case of the control task. The principles of the developed controllers are introduced next. The details of the controllers are described in [P7]. Automatically tuning controller. The principal target of the designed automatically tuning control system was that the controller could ‘learn’ the ‘right’ DS class selection and price-setting strategy automatically so that the control aims of the ISPs would be fulfilled. For achieving this aim, three principal phases were implemented. These phases were 1) classification of the control situations and definition of alternative control actions, 2) tuning of the controller and 3) normal use of the controller.

ISPs advertise their SLAs to the customers. IP domains of the ISPs include the SLA management system for setting prices and the DS classes for the SLAs

The customers select the SLAs according to their personal interests. After sending of data flows, the source terminals of the customers send a response to the selected ISPs regarding suitability of the offered data transmission services.

Source terminals

Destination terminals

Access network

IP domain of the ISP

Backbone networks

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Fig. 16. The state-changing diagram of the controller.

At the beginning of use of the controller, the first phase of the controller was carried out. After that, the controller performed the ‘tuning phase’ and the ‘using phase’ iteratively. The state of the controller changed from the ‘tuning phase’ to the ‘using phase’ always after nL learning rounds. Correspondingly, the state changed from the ‘using phase’ back to the ‘tuning phase’ after the interval tC=nR*tR, where nR is the number of control moments and tR is the constant interval between control moments. The number of the control moments nR varied randomly inside the constant value range. The state-changing diagram of the controller is described in Fig. 16.

In the ‘classification of the control situations and definition of alternative control actions’ phase, possible control situations were classified using the SOM network. For the classification operation, the ISPs defined all possible control situations using the special environment variables. They also defined the sizes of the SOM networks used. After the classification, the nodes of the SOM networks represented the ‘known’ control situations of the controllers. The output field was the SDS * SPRICE network, where SDS indicates the number of alternative DS classes and SPRICE indicates the number of alternative prices of the SLAs. The ISP defined minimum and maximum values of DS classes and prices of the SLAs, and the value of the SPRICE. After that, values of nodes of the output field were calculated so that alternative value sets of DS classes and prices of SLAs were spread smoothly over nodes of the network.

During the ‘tuning of the controller’ phase, the control actions were defined for every classified control situation so that the control targets of the ISPs were fulfilled as well as possible. In practice, the tuning process defined optimal links between nodes of the input and output fields. Using these links, the controller could select the DS classes for data flows of the SLAs and set prices of the SLAs. The tuning task was done using the genetic algorithms. The controller is illustrated in Fig. 17.

During the using phase, the controller defined iteratively DS classes and selling prices for all alternative SLAs at the interval tR. At every control moment, values of the environment variables were first calculated according to the measured information between two sequential control moments. After that, the best fitting classified control situation was sought from the SOM network for the current control situation. The link between the chosen classified control situation and the output field (defined in the tuning

After nL learning rounds

Classification of the control situations

Tuning phase Using phase

After the interval tC

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phase) indicated which DS class was used for data flows of controlled SLA and what was the price of the controlled SLA during the next interval tR.

Fig. 17. Fields of the automatically tuning controller.

Manually tuned controller. The principal idea of the control system based on manually tuned fuzzy controllers was contrary to the idea of the above-described automatically tuning controller. When the automatically tuning controller tried to learn the right control actions according to experimental control results, the fuzzy controller based control system was based on the control experience of the system experts. In the fuzzy controller based system, the system experts tuned the fuzzy controllers according to their control knowledge so that the control aims of the ISPs would be fulfilled. Other principal characteristics of the fuzzy controller based system were independence, simplicity and scalability. The fuzzy controllers included only three input variables and the ingress routers were able to measure numerical values of all input variables.

The control system included two fuzzy controllers. One of these controllers selected DS classes for the defined SLAs and another one set selling prices of the SLAs. Both controllers had three equal input variables and one output variable. The input variables were: 1) popularity of the ISP, 2) expected relative quality of the used DS class, and 3) relative financial profit of the ISP. The significance of the input variables are discussed carefully in [P7]. The output variable of the ‘DS class selection’ controller indicated how a ‘good’ DS class, in relation to the statistically best possible DS class, should have been selected for the controlled SLA. Correspondingly, the output value of the ‘price adjusting’ controller indicated the price level of the controlled SLA, in relation to the defined maximum price.

The fuzzy controllers were tuned before use of the control system. After the tuning process, the control system was immediately ready for use. The ‘using phase’ of the fuzzy controller based control system was similar to the ‘using phase’ of the automatically tuning control system. At every control moment, DS classes were selected and prices were set for all defined SLAs of the ISPs. During control of each SLA, the

Input field: classified control situations

Output field: Control actions of the controller

Links between nodes of the input and the output fields

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numeric values of the input and output variables of the fuzzy controllers were calculated. After these calculations, the DS class and the price of the SLA were defined.

5.2. The latest studies In the two last publications, the main target was to study different ways for obtaining the opinions of customers regarding the offered data transmission services. In [P8] it was assumed that the ISPs do not get any feedback from their customers. In contrast, the controller in [P9] assumed absolutely that the source terminals of the customers send feedback information about the suitability of the services back to the ISPs of the selected services. In fact, it could be argued that the controller working without any feedback information would be easier to implement in real network environments than the controller using the feedback information. In the feedback case, the customers should be willing to give the feedback information, the protocol between the source terminals and the ingress routers should be defined and the source terminals should include accessory programs for computing and sending the feedback.

The control systems of both studies used a similar control architecture. The SLA management system included the initialization and the using phases. In the initialization phase, the ISPs initialized their SLA management systems. After the initialization phase, systems controlled the SLAs periodically. At every control moment, a selling price and a DS class of one SLA were set. After the control actions, the advertisement information of the controlled SLA was updated. The controlled SLA was randomized using uniform distribution. This strategy ensured that all SLAs had equal probabilities to be controlled. The price and the DS class of the controlled SLA were constant between two sequential control moments of the SLA. The information needed for the control of the SLA was collected between sequential control moments of the SLA. The control procedure is described in Fig. 18.

Fig. 18. The control procedure.

Phase 1: Initialization

Random interval

Random interval

Phase 2: Random selection of the next controlled SLA

Phase 3: Definition of a selling price and a DS class for the controlled SLA.

Phase 4: Updating the advertisement information of the controlled SLA.

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The principal element of the SLA management systems was a two-dimensional state-chart (see Fig. 19). The state-chart consisted of P alternative selling price (EUR/data unit) and Q codepoint values. The ISPs defined M service level agreements SLA(pi,qi), where i ∈ [1,M], p ∈ [1,P] and q ∈ [1,Q]. Each pair (p,q) represents a state of the state-chart consisting of 1) cost for the ISP, 2) an ‘opinion’ database, 3) a ‘data’ database and 4) a ‘moving’ database. The cost for the ISP indicated the average internal cost for the company to arrange an Internet connection with service level q for a customer. The ‘opinion’ database stored the opinions of the customers regarding the offered data transmission services in state (p,q). The controllers of [P8] and [P9] used different variables for observing opinions of the customers. The controller of [P9] was based on the satisfaction values of the customers. The customers were allowed to send the satisfaction values to the ISPs after data transmissions. The numeric value range of the values was [0, MaxSR]. The ISPs defined the value of the MaxSR factor. The satisfaction of the customers increased as a function of the satisfaction value. On the other hand, the controller of [P8] did not expect any feedback from the customers. In this case, statistical information about the use of the SLAs was used for indicating satisfaction of the customers. It was simply assumed that the satisfaction of customers increases when the use of the SLAs increases.

Another important difference between the two studies is the weighting of the opinions of the customers. In the controller of [P9], opinions of the customers could be weighted according to the strategies of the ISPs. These weight values enable the ISPs to consider the importance of the opinions of different customers. For example, the opinions of customers who use the data transmission services frequently may be more important than the opinions of standard customers. Because the controller of [P8] observed the behaviour of customers just statistically, it could not consider opinions of individual customers.

The ‘data’ database stored information about the amount of transferred data of customers using the SLAs. Every record of the database stored the amount of the data of customers (Mbit) transferred during the latest control interval. The ‘moving’ database stored the changes of the opinions of customers and the amount of transferred data of the SLAs, if the location of SLAs changed from state (p,q) to the classified directions on the state-chart.

The management systems defined periodically a selling price and a codepoint value for the SLAs according to the control aims of the ISP. Practically speaking, the controllers changed the location of the defined SLAs on the state-chart. At each control moment (phase 3 in Fig. 18), the systems calculated suitability values of alternative states for the controlled SLA. If the databases of the states included enough information for the fuzzy controller, the fuzzy controller was used for calculating the suitability values of the states. Otherwise, the suitability values of the states were randomized from the uniform distribution. The state whose suitability value was the largest was selected for the next state of the SLA on the state-chart. The selling price and the DS class (codepoint) of the selected state were used for the SLA. In both studies, the fuzzy controller contained two input variables and one output variable. The input variables were: 1) satisfaction ratio and

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2) profit ratio. The ‘Satisfaction ratio’ compared the expected and the estimated average satisfaction level of customers using the controlled SLA if the state of the state-chart had been selected for the controlled SLA. The ‘Profit ratio’ compared the expected and the estimated financial profit of the ISP (EUR/s). The output variable indicated suitability levels of states. Calculation of the numeric values of the input variables and definition of the alternative states varied between [P8] and [P9], according to the different principles of the controllers (see [P8] and [P9]).

Fig. 19. The structure of the controllers in the latest studies.

(Q) alternative DS classes (codepoints) for the data flows of the SLAs

(P) alternative prices of the SLAs

Each state (p,q) included the following databases.

1) Costs of the ISP (EUR/data unit)

2) An ‘opinion’ database which stored opinions of the customers regarding the offered data transmission services if price (p) and codepoint (q) were used for data flows. In [P9], every stored opinion was also weighted according to the importance of the customers

3) A ‘data’ database which stored the amount of transferred data of the state

(p,q).

4) A ‘moving’ database. It stored changes in the opinions of the customers and

the amount of transferred data if the location of SLA changed from state (p,q) to the classified directions on the state-chart.

Eight classified moving directionsof SLAs.

THE STATE-CHART

The ISPs defined M SLAs for the customers. Controllers of [P8] and [P9] adjusted periodically locations of the SLAs on the chart, according to the control aims of the ISPs.

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5.3. The summarized results of the studies Comparison of controllers. In [P7], an important question is what kind of control system would be the best for the control task. The answer to this question is ambiguous. Both control systems of [P7] offered the relatively easy interface for the ISPs. However, the tuning of a fuzzy controller based system required some basic knowledge about fuzzy controllers, although the fuzzy controller could be tuned using some graphical interface. From this point of view, the automatically tuning controller was easier to use. The ISPs just defined their control aims and the controller optimized the control actions so that the control aims of the ISPs were fulfilled as optimally as possible. Furthermore, if the ISPs had the need to observe control decisions of the automatically tuning controller, understanding the meaning of links between nodes of input and output fields of the control system was relatively easy.

According to the test results of [P7] selection of the input variables of the controllers affected the usability of the controllers. If the ISPs controlled both their financial profit and the quality of transmission services offered to their customers, the automatically tuning control system was satisfactory. It included required input variables for the control task and it was able to adapt to different control targets of the ISPs. The principal weakness of the automatically tuning controller was the long tuning period. The controller required a long tuning period before it produced good control actions for all control situations. In fact, this is a well-known weakness of systems based on genetic algorithms. However, it can be expected that this characteristic of the control system does not debar use of the system, because the operation periods of networks are usually also long.

The fuzzy controller based control system was satisfactory for control situations in which the DS classes and prices of the SLAs had to be set with a minimum amount of information about the control environment. It did not require any information from the customers and the control actions were based just on information of three input variables. For these reasons, the controller produced satisfactory control results reliably and autonomously. Unfortunately, the independence of the control system caused one significant problem. The control system was not able to consider quality of transmission service because it did not include an input variable for this purpose. Results of the latest studies. The control systems presented in [P8] and [P9] both achieved satisfactory control results. In both studies, the control systems could control prices and transmission services used for SLAs according to the targets of the ISPs. The control results were satisfactory even if the targets of the ISPs varied greatly (see [P8] - [P9]). Using the fuzzy controllers, it was possible to define very different service selection and price-setting strategies. Two examples of different price-setting and service selection strategies are given in Figures 20-21. The fuzzy controller represented in Fig. 20 calculated the highest suitability values for the states which produced maximum profits for the ISPs and good service for the customers. On the other hand, the fuzzy controller presented in Fig. 21 calculated high suitability values for the states which should ensure good service for the customers with just the expected financial profit of the ISPs. In the figures, the values of the ‘profit ratio’ and ‘user satisfaction ratio’ factors

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which are greater than one indicate that the values of the factors are better than the nominal expectation of an ISP. For example, if the value of the ‘profit ratio’ factor is 2.3, it means that the financial profit is 2.3 times greater than the nominal profit set by an ISP. Both strategies were tested in the studies of [P8] – [P9]. Obviously, the strategy of Fig. 21 is quite academic. What kind of ISP would not like to maximize the financial profit? However, the case indicated that the controllers were able to tune for different strategies. Fig. 20. The first example of the price-setting and service selection strategy. Fig. 21. The second example of the price-setting and service selection strategy.

PROFIT RATIO

SUITABILITY VALUE

USER SATISFACTION RATIO

SUITABILITY VALUE

PROFIT RATIO USER SATISFACTION RATIO

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5.4. Discussion of the problems and the solutions From the theoretic point of view, dynamic agreements with adaptively changing prices and service quality should be satisfactory for both ISPs and customers. These kinds of dynamic agreements would ensure an open market environment, where prices and quality of services would change dynamically according to current market situations. The customers could select data transmission services from several alternative service providers according to their current requirements. In this way, the customers could optimize costs and the quality of data transmissions. From the perspective of ISPs, the strategy would make it possible to consider dynamically data transmission needs of customers and data transmission services of other ISPs.

Despite the benefits of the dynamic agreements studied, use of these agreements in a real network environment is not a simple issue. Today, agreements between customers and ISPs are quite long-term. The long-term agreements are straightforward for both customers and ISPs. The customers may benefit more from long-term agreements with fixed monthly costs and known QoS than from the possibility to adaptively choose their services. From the perspective of customers, it is convenient to use services whose costs do not depend on the amount of transferred data. However, dynamic agreements with per-packet based pricing could be satisfactory for customers who are not willing to commit for long-term agreements. These customers may use transmission services so seldom that long-term agreements would be too expensive for them. The model of dynamic agreements studied would provide the possibility to optimize the costs of this type of customers. Also the manner in which services are selected must be designed carefully. It could be argued that the selection of the dynamic agreements should be automatic. It would be naive to think that customers would be willing to manually choose their services for every data transmission. Simply, it would be too boring a task. One possible scheme for implementing automatic service selection is introduced later in this section.

The long-term agreements are also useful for the ISPs. They can do their financial and technical planning easily when the number of customers and the average amount of traffic are known for a long period. In addition, implementation of the dynamic agreements would require additional functions, which would complicate the management of networks. For these reasons, it could be argued that the ISPs may not be willing to use dynamic agreements. At least, the ISPs should obtain financial benefits through offering them to customers.

It is important to mention that the developed agreement management systems can be used also for long-term agreements. The control systems do not require any specific control frequency. They can be adapted to different SLA establishing frequencies by setting the length of the control interval and the sizes of the ‘behaviour’ databases. In this way, the developed systems can be used even if the agreement establishing frequencies varied from individual transmission to years. The systems can also include other parameters for agreements than a price and a DS class. In this way, the system enables the control of characteristics of larger service packages, including e.g. help desk and security services. The control systems of [P8] – [P9] can handle these kinds of additional

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parameters, when the dimension of ‘the state-chart of the control solutions’ is increased according to the number of additional parameters.

5.4.1. Auction-based solution for ISPs Sharing of the transmission capacity between long-term and dynamic agreements could be one possible option in making dynamic agreements more interesting to ISPs. In this model, ISPs would sell the available capacity of their networks, left over data flows of long-term agreements, using dynamic agreements. In this way, the unused capacity could be sold using one kind of auction process. ISPs would certainly obtain more profit compared to the situation where the unused capacity is not sold. Obviously, implementation of this strategy requires that DS-enabled networks have different high priority services for both long-term and dynamic agreements. The priority of services of long-term agreements should be higher than the priority of services of dynamic agreements.

5.4.2. Automatic selection process for customers As mentioned above, customers would require an automatic system for service selection. This system could operate in source terminals or edge routers of access networks. Before each data transmission, the system would select a suitable agreement for the customer. The main tasks of the system would be collection of information about possible agreements and selection of a suitable agreement for data transmission. Collection of information would require some protocol between source terminals and ISPs. This protocol would transfer recent information about the available agreements from the ISPs to the source terminals. The service selection procedure should select the agreements so that financial aims and QoS requirements of customers are fulfilled optimally. It should consider QoS requirements of network-oriented applications, prices of the agreements offered, promised QoS of the agreements offered, opinion of the customer regarding a suitable relation of prices and QoS and statistical information about the reliability of the QoS promises of the ISPs.

According to the spirit of this thesis, soft-computing methods could be proposed also for implementing the service selection procedure. For instance, the system could include a fuzzy controller which would calculate suitability values for every agreement offered according to the above-mentioned criteria. The agreement whose suitability value is the best, would be selected for current transmission. The controller could include three input variables. The first input variable could measure how well the promised QoS corresponds with the QoS requirements of an application and QoS expectations of customers. The second input variable could measure how well the relation of prices and promised QoS satisfy the expectations of customers. The third input variable could indicate how reliable the QoS promises of the ISPs have been in the past. The output variable of the controller could indicate the suitability of the investigated agreement for the current transmission task. In this way, the fuzzy controller would implement a three-

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dimensional non-linear decision function which would select services for customers. Rough-tuning of the controller could be done by customers. For this task, the system should include an interface whereby the customer can tune the controller without knowledge of fuzzy controllers. After the rough-tuning, fine-tuning of the controller could be done as an automatic process. For example, genetic algorithms could be used for the fine-tuning task. The layout of the controller is described in Fig. 22.

Fig. 22. The layout of the controller which make service selection decisions for end-users.

6. Conclusions This thesis comprises two individual research subjects. The first research subject of the present thesis concentrated on optimizing the rate control task of assured service in the B-ISDN type of networks. In the second research subject of the thesis, control systems were developed for optimizing the price settings and service selections of ISPs in DS-enabled IP network environment. Although the control tasks and the network environments studied were different, the primary aim of the research topics was the same: to optimize the welfare of both network operators and end-users.

The meaning of control in the first research item is unambiguous. End-users and network operators both benefit if the available capacity of networks, after a load of high

Fuzzy controller

INPUT 1: Suitability of QoS of the service offered for a recent application

INPUT 2: Suitability of relation between promised QoS and the price of an agreement

INPUT 3: Reliability of the QoS promises of the ISP whose agreement is examined.

OUTPUT: Suitability of the examined agreement for an application.

Rough-tuning of the controller could be done by the customers.

Fine-tuning could be done using an automatic tuning process.

User-friendly Interface

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priority traffic, can be shared optimally between the connections of the assured service. Networks are utilized optimally and the goodput of connections is maximized.

The systems of the second research item solved a similar optimization task in different network environment and in a different way. In this case, the developed systems set prices and selected DS classes of the SLAs so that the aims of the ISPs would be optimized. However, the behaviour of customers affected the control decisions of the systems, and customers could freely select agreements and ISPs for their data transmission needs. In this kind of open market environment, the developed systems could ensure also customer satisfaction, even if the aims of the customers varied. It can be assumed that most ISPs would like to maximize their financial profit. To achieve this aim, they must tune the controllers so that the targets of the customers are considered.

The studied open market environment with the dynamic agreement negotiations is not the recent reality. However, the systems can be used, even if the frequency of agreement negotiations and control of the characteristics of the agreements vary greatly. From this perspective, the developed systems are scalable for different situations. The controllers of both research items worked in highly non-linear environment, where all required information for control decisions was not known, or information was already old. In the studies, various soft-computing methods were used for solving the control tasks. The primary reason for this selection was the well-known benefits of soft-computing methods just for these kinds of control environments. Complexity of the tested soft-computing methods varied from simple manually tuned fuzzy controllers to relatively complex automatically tuning systems.

The suitability of different soft-computing schemes is an important question. How complex may the schemes be which it is reasonable to use for the network control tasks studied? According to the test results, good results were achieved using both complex and simple schemes. It could be argued that the complex systems have more potentialities for achieving optimal control results than the simple ones, but the complex systems must be designed and used very carefully to achieve optimal control results. From this perspective, it could be proposed to use the simplest possible soft-computing method and increase the control frequency, if necessary. In the control environments, where several issues are changing dynamically, it is more important to produce logically correct than numerically exact control decisions. On the other hand, it may be easier to increase the frequency of simple control computations than the computation capacity of network components for complex control schemes. If complex algorithms are used, the components performing the control computations must be selected carefully. According to the results of the present thesis, edge nodes and user terminals are more suitable for complex computations than core nodes.

In the studies, the simple manually tuned Mamdani type fuzzy controller was found to be an especially suitable tool for different control tasks. This type of controller was able to solve non-linear control problems, but it was also simple enough to be used in the network environments studied.

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7. Summary of publications The publications can be divided into two categories. Publications in the first category, [P1] – [P6], deal with the control of assured service. In these studies, different control systems were developed for optimizing the use of the available resources of the networks, after the load of higher priority data flows. Publications of the second category, [P7] – [P9], concentrated on price setting and service selection problems of the ISPs in DS enabled IP networks. In these publications, special controllers were developed for finding the optimal combination of the QoS levels and prices of advertised data transmission services. The systems set prices and DS classes of the SLAs according to operation targets of the ISPs. The management systems were designed for a future network environment in which customers could select their ISPs and SLAs dynamically and freely, irrespective of their access network operators.

In all studies, different soft-computing based controllers were used for solving the control tasks. The developed control systems were tested using simulations. In the studies of [P1] – [P6], a discrete-event based simulator was used. This simulator was designed especially for the studies of the publications. In the study of [P7], the control operations were tested using Matlab®. The reason for this selection was the use of relatively complex control calculations. In the studies of [P8] – [P9], network simulator 2 (NS-2) [183] was used for testing purposes.

7.1. Overview of publications Publication 1, [P1] J. Harju, K. Pulakka: Optimisation of the Performance of a Rate-based Congestion Control System by Using Fuzzy Controllers. 18th IEEE International Performance, Computing, and Communications Conference, IPCCC 1999. Phoenix/Scottsdale, Arizona, U.S.A. February 10-12, 1999. pp. 192-198. Description. Simple manually tuned Mamdani-type fuzzy controllers were used for controlling data rates. The control actions were performed both on the network nodes and on user terminals. The nodes and destination terminals included a Mamdani-type fuzzy controller which was tuned manually. The fuzzy controllers of the nodes observed buffer occupancy levels of output ports of the assured service and calculated how much data rates should be decreased or increased to achieve the optimal occupancy level. The fuzzy controllers of the destination terminals decided if there was a serious need to send the special control cells to the source terminals of the connections. The source terminals adjusted data rates according to information of the special control cells. The principle role of the publications was to present the structure and the performance results of the system.

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Publication 2, [P2] J.Harju, K.Pulakka: Fuzzy Logic Rate Control for the ABR Service Category in B-ISDN Networks. 1999 American Control Conference, ACC99, San Diego, California, USA, June 2-4, 1999. pp. 4446-4450. Description. This publication concentrated on the equal rate control system as [P1]. However, the publications concentrated on different issues. The principle aim of [P2] was to describe the selections and equations made for implementing the controllers of the system. Publication 3, [P3] K. Pulakka, J.Harju: Optimization of the Utilization of a Packet Switched Backbone Network by Using a Control System for Low Priority Traffic, 19th IEEE International Performance, Computing, and Communications Conference, IPCCC 2000, Phoenix, Arizona, USA, February 20-22, 2000, pp. 124-131. Description. The performance of the first control system, described in [P1] and [P2], was satisfactory. However, the developed system included controllers in the core nodes. This was not a satisfactory scheme, because transmission speeds of core nodes were (and are still) increasing greatly. For this reason, the next development step was to move the control operations of the core nodes to the edge nodes of the networks. The second change was to use several alternative paths between the edge nodes for transferring data of the controlled assured service. Thirdly, the system included two alternative strategies for adjusting the data rates ; the explicit and the iterative adjustment strategies. Moving of the control calculations to edge nodes simplified the control operations of core nodes. Roughly speaking, core nodes only measured the load of output links and copied this information to the special resource management cells. The control decisions were still based on the use of manually tuned Mamdani-type fuzzy controllers. Publication 4, [P4] K.Pulakka, J.Harju: Distributed Control System for Low Priority Controllable Traffic in Packet Switched Backbone Networks, Networking 2000, IFIP-TC6/European Commission International Conference, Paris, France, May 2000, pp. 596-607. Description. The control system of [P4] is an advanced version of the control system of [P3]. In the case of an explicit data rate adjustment, the prediction of the load of high priority traffic was more complicated than in the system of [P3]. An ANFIS neuro-fuzzy predictor took the place of a simple linear predictor. The reason for this decision was to test the performance of a non-linear predictor for the prediction task. According to the studies of other research groups, ANFIS had been an effective soft-computing method for predicting highly non-linear systems. However, the system included also the iterative rate control scheme, implemented using the fuzzy controllers.

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Publication 5, [P5] K. Pulakka, J. Harju: Efficiency of the prediction of high priority traffic in enhancing the rate based control of low priority traffic. Smartnet 2000 conference, Telecommunication Network Intelligence, Vienna, Austria, September 18 - 22, 2000, pp. 181-196. The primary aim of the study of [P5] was to test the performance of ANFIS in the prediction task. For this reason, the system no longer included iterative fuzzy logic based control operations, as the older control system did. The free capacity for the connections of the assured service was simply calculated periodically by predicting the load of the high priority traffic using ANFIS. The control system included several timers which affected the collection of the control information from core nodes and the control frequencies of the edge nodes. The effects of different values of these timers on the performance of the system were tested. Furthermore, a well-known fact is that the size of the ANFIS network affects the function approximation performance of the network. For this reason, the effects of different sizes of ANFIS networks on the performance were also tested. Publication 6, [P6] K. Pulakka, J. Harju: Comparison of Different Congestion Control Strategies for Low Priority Controllable Traffic in Packet Switched Backbone Networks. The International Journal of Communication Systems, Vol 14, John Wiley & Sons, Ltd., 2001, pp. 813 - 836. This publication has a special role in this thesis. It finalised the research work of the first research category. In the study of this publication, different data rate control schemes for assured service were compared. The main objective of this publication was to compare the technical performance of the congestion control strategies with respect to their efficiency in the sense of the required computation. From this point of view, we studied what kind of congestion control strategy would have been optimal to achieve the general aims of the assured service. The major difference between the study of this publication and the studies of [P3]-[P5] was that the control system did not select paths for the data flows dynamically. Constant paths were used for the data flows during whole transmissions. This selection was made to ensure easier comparison of the performance of the tested control methods.

Four congestion control methods were chosen for the comparison study. One of them was based on both the occupancy information of the output buffers and the load changes of the high priority traffic. The other three were based on different load prediction methods for the high priority traffic. The congestion control methods which were based on the load prediction of the high priority traffic differed from each other by the complexity of their prediction algorithms.

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Publication 7, [P7] Pulakka, K. Performance of Two Different Soft-Computing Based Controllers for Service Selection and Price Adjusting Task of the ISPs of the DS-enabled Internet. Proceedings of the 2002 IEEE International Symposium on Intelligent Control, October 27-30, 2002, Vancouver, Canada. pp. 282-289. Description. In this study, we studied the usability of two different soft-computing based control systems to solve service selection and price setting tasks. The first control system was based on the use of simple manually tuned Mamdani-type fuzzy controllers, while the second system used SOFM (Self-organized Feature Map) and genetic algorithms (GA) for implementing an automatically tuning controller for the control tasks. The fuzzy controller based control system was designed so that the minimum amount of information was used for control actions, while the automatically tuning controller used several input variables. From this point of view, we compared the performance of very simple and relatively complex controllers for the control task. Publication 8, [P8] Pulakka, K. A Dynamic Control System for Adjusting Prices and Quality of Service in DS-enabled Networks. Proceedings of Conference on Network Control and Engineering of QoS, Security and Mobility (Net-Con 2002), October 23-25, 2002, Paris, France, pp. 241-252. Publication 9, [P9] Pulakka, K. Controlling of satisfaction of the end-users and profits of the ISPS in the DS enabled Internet. Proceedings of the 8th International Conference on Communication Systems, ICCS 2002, November 25-28, 2002, Singapore. 7 p. Description of [P8] and [P9]. In these two last studies, the main target was to study different ways for taking into account opinions of customers concerning the offered data transmission services. Information about the opinions of customers was used for setting prices and selecting DS classes of the offered SLAs. In the study of [P8], it was expected that the ISPs do not obtain any explicit feedback from their customers. In contrast, the controller of [P9] assumed absolutely that the source terminals of the customers send feedback information about the suitability of the services back to the ISPs of the selected services. In both studies, manually tuned Mamdani-type fuzzy controllers were used for producing control decisions. Using the fuzzy controllers, the ISPs were able to define their individual control targets for the system.

7.2. Author’s contribution to the publications In the case of all research works of this thesis, the author had a major role in developing the ideas, verifying their performance by simulations and writing the publications. Professor Jarmo Harju has been the mentor of the author. Professor Harju verified the

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technical solutions of the systems of [P1] – [P6]. He also took part in the writing of these publications. In the writing process of [P1] – [P2], role of Processor Harju was that of structuring and writing the publications. In the writing process of [P3] – [P6], Professor Harju worked in the role of the ‘second writer’.

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Bibliography [1] International Telecommunication Union (ITU-T), Recommendations I.311, I.321, I.327 [2] Kilkki K., Differentiated services for the Internet, Macmillan Technical Publishing, June 1999, Section 5.1 [3] Long-Tae Park; Jong-Wook Baek; Woon-Ki Hong, J.; Management of service level agreements for multimedia Internet service using a utility model, IEEE Communications Magazine, , Volume: 39 , Issue: 5 , May 2001 [4] WWW page of International Telecommunication Union, http://www.itu.int/home/ [5] The ATM Forum, Technical Committee, Traffic Management Specification, Version 4.1, AF-TM-0121.000, March 1999 [6] WWW page of Internet Engineering Task Force, http://www.ietf.org [7] Bernet, Y., The complementary roles of RSVP and differentiated services in the full-service QoS network, IEEE Communications Magazine, Volume: 38 , Issue: 2, Feb. 2000, On page(s): 154 - 162 [8] Xipeng Xiao; Ni, L.M. , Internet QoS: a big picture, IEEE Network, March-April 1999, Vol. 13, pp. 8-18. [9] S. Blake, D. Black, M. Carlson, E. Davies, Z. Wang, W. Weiss; An Architecture for Differentiated Services, Internet Engineering Task Force (IETF), Network Working Group, RFC 2475, December 1998 [10] B. Davie, A. Charny, J.C.R. Bennett, K. Benson, J.Y. Le Boudec, W. Courtney, S. Davari, V. Firoiu, D. Stiliadis; An Expedited Forwarding PHB (Per-Hop Behavior), Internet Engineering Task Force (IETF), Network Working Group, RFC 3246, March 2002 [11] J. Heinanen, F. Baker, W. Weiss, J. Wroclawski; Assured Forwarding PHB Group, Internet Engineering Task Force (IETF), Network Working Group, RFC 2597, June 1999 [12] P. Bhoj, S. Singhal and S. Chutani, SLA management in federated environments, Computer Networks, Volume 35, Issue 1, January 2001, Pages: 5-24

78

[13] Mischa Schwartz, “Broadband Integrated Networks”, Prentice Hall PTR, Upper Saddle River, New Jersey 07458, 1996 [14] Mark E. Crovella and Azer Bestavros, Self-Similarity in World Wide Web Traffic: Evidence and Possible Causes, IEEE/ACM Transactions on Networking, Vol. 5, No. 6, December 1997, pp. 835-846 [15] Prasad, A.R. Stavrov, B. Schoute, F.C., Generation and testing of self-similar traffic in ATM networks, IEEE International Conference on Personal Wireless Communications, 19-21 Feb. 1996, New Delhi India, On page(s): 200 - 205 [16] Raj Jain, The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling, John Wiley & Sons inc., 1991, Section 24.5.3 [17] Ma, Q.; Steenkiste, P., Supporting dynamic inter-class resource sharing: a multi-class QoS routing algorithm, INFOCOM '99, Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. 21-25 March 1999. Vol. 2, pp. 649 – 660. [18] EU IST (European Information Society Technologies), http://www.cordis.lu/ist/home.html [19] M. Brunner (Editor), ” Requirements for Signaling Protocols”, draft-ietf-nsis-req-09.txt, Internet Engineering Task Force (IETF), Network Working Group, Internet Draft, August 2003 [20] Robert Hancock (editor), Ilya Freytsis, Georgios Karagiannis, John Loughney, Sven Van den Bosch, “Next Steps in Signaling: Framework”, Document: draft-ietf-nsis-fw-02.txt, Internet Engineering Task Force (IETF), NSIS Working Group, Internet Draft, March 2003 [21] H. Tschofenig, D. Kroeselberg, “Security Threats for NSIS”, draft-ietf-nsis-threats-03.txt, Internet Engineering Task Force (IETF), NSIS Working Group, Internet Draft, October 2003 [22] Hannes Tschofenig, Richard Graveman, “RSVP Security Properties”, draft-ietf-nsis-rsvp-sec-properties-03.txt, Internet Engineering Task Force (IETF), NSIS Working Group, Internet Draft, October 2003 [23] J. Manner, X. Fu, “Analysis of Existing Quality of Service Signaling Protocols”, draft-ietf-nsis-signalling-analysis-01.txt, Internet Engineering Task Force (IETF), Internet-Draft, February 2003

79

[24] D. Awduche, L. Berger, D. Gan, T. Li, V. Srinivasan, G. Swallow; RSVP-TE: Extensions to RSVP for LSP Tunnels, Internet Engineering Task Force (IETF), Network Working Group, RFC 3209, December 2001 [25] L. Berger, “Generalized Multi-Protocol Label Switching (GMPLS) Signaling Resource Reservation Protocol-Traffic Engineering (RSVP-TE) Extensions”, Internet Engineering Task Force (IETF), Network Working Group, RFC 3473, January 2003 [26] D. Awduche, A. Chiu, A. Elwalid, I. Widjaja, X. Xiao; Overview and Principles of Internet Traffic Engineering, Internet Engineering Task Force (IETF), Network Working Group, RFC 3272, May 2002, page 52 [27] Ping Pan, Henning Schulzrinne, "YESSIR: A Simple Reservation Mechanism for the Internet". In the Proceedings of NOSSDAV, Cambridge, UK, July 1998. [28] G. Feher, K. Nemeth, M. Maliosz, I. Cselenyi, J.Bergkvist, D. Ahlard, T. Engborg, "Boomerang A Simple Protocol for Resource Reservation in IP Networks", IEEE RTAS, 1999. [29] S. Lee, A. Gahng-Seop, X. Zhang, A. Campbell,"INSIGNIA: An IP-Based Quality of Service Framework for Mobile Ad Hoc Networks". Journal of Parallel and Distributed Computing (Academic Press), Special issue on Wireless and Mobile Computing and Communications}, Vol. 60, Number 4, April, 2000, pp. 374-406. [30] P. Pan, E, Hahne, and H. Schulzrinne, "BGRP: A Tree-Based Aggregation Protocol for Inter-domain Reservations", Journal of Communications and Networks, Vol. 2, No. 2, June 2000, pp. 157-167. [31] L. Delgrossi and L. Berger, Internet Stream Protocol Version 2 (ST2) Protocol Specification - Version ST2+, Internet Engineering Task Force (IETF), Network Working Group, RFC 1819, August 1995 [32] IPPM (IP Performance Metrics) Working Group, http://www.ietf.org/html.charters/ippm-charter.html [33] Giordano, S. Salsano, S. Van den Berghe, S. Ventre, G. Giannakopoulos, D., Advanced QoS provisioning in IP networks: the European premium IP projects, IEEE Communications Magazine, Volume: 41, Issue: 1, Jan 2003, On page(s): 30- 36 [34] G. Almes, S. Kalidindi, M. Zekauskas, ” A One-way Packet Loss Metric for IPPM”, Internet Engineering Task Force (IETF), Network Working Group, RFC 2680, September 1999 [35] G. Almes, S. Kalidindi, M. Zekauskas, ” A One-way Delay Metric for IPPM”, Internet Engineering Task Force (IETF), Network Working Group, RFC 2679, September 1999

80

[36] J. Mahdavi, V. Paxson, ” IPPM Metrics for Measuring Connectivity”, Internet Engineering Task Force (IETF), Network Working Group, RFC 2678, September 1999 [37] Aquila (Adaptive Resource Control for QoS Using an IP-based Layered Architecture), Part of European IST (Information Society Technologies) program, http://www-st.inf.tu-dresden.de/aquila/, [38] Tequila (Traffic Engineering for Quality of Service in the Internet, at Large Scale), Part of European IST (Information Society Technologies) program, http://www.ist-tequila.org/ [39] Cadenus (Creation and Deployment of End-User Services in Premium IP Networks), Part of European IST (Information Society Technologies) program, http://www.cadenus.org/, [40] Shivkumar Kalyanaraman, Associate Member, IEEE, Raj Jain, Fellow, IEEE, Sonia Fahmy, Member, IEEE, Rohit Goyal, and Bobby Vandalore, The ERICA Switch Algorithm for ABR Traffic Management in ATM Networks, IEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 8, NO. 1, FEBRUARY 2000, pp. 87-98. [41] Ahmet Sekercioglu, Y.; Pitsillides A., Intelligent control techniques for efficient regulation of ABR queue length in ATM switches, Second IEEE Symposium on Computers and Communication. 1-3 July 1997, pp. 80-84. [42] Pradhan, P.; Tzi-cker Chiueh; Neogi, A., Aggregate TCP congestion control using multiple network probing, 20th International Conference on Distributed Computing Systems, 10-13 April 2000, pp. 30-37. [43] Kunniyur, S.; Srikant, R. , End-to-end congestion control schemes: utility functions, random losses and ECNmarks, INFOCOM 2000. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, 26-30 March 2000. Vol. 3, pp. 1323 - 1332 . [44] Durresi, A.A. Sridharan, M. Chunlei Liu Goyal, M. Jain, R., Congestion control using multilevel explicit congestion notification in satellite networks, Tenth International Conference on Computer Communications and Networks, 2001, 10/15/2001 -10/17/2001, Scottsdale, AZ , USA, On page(s): 483-488 [45] Kalampoukas, L. Varma, A. Ramakrishnan, K.K., Explicit window adaptation: a method to enhance TCP performance, IEEE/ACM Transactions on Networking, Volume: 10, Jun 2002, On page(s): 338-350

81

[46] Xiaobing Leng Lizheng Fu Guixing Luan, A new EFCI scheme with ability of periodically setting for ABR flow control in ATM networks, Joint 4th IEEE International Conference on ATM (ICATM 2001) and High Speed Intelligent Internet Symposium, 2001, 04/22/2001 -04/25/2001, Seoul , South Korea, On page(s): 128-132 [47] Chiussi, F.M. Ye Xia Kumar, V.P., Dynamic max rate control algorithm for available bit rate service in ATM networks, Global Telecommunications Conference 1996, GLOBECOM '96, 18-22 Nov 1996, London , UK, On page(s): 2108-2117 vol.3 [48] Kolarov, A. Ramamurthy, G , Comparison of congestion control schemes for ABR service in ATM local area networks, IEEE Global Telecommunications Conference, GLOBECOM '94, 28 Nov.-2 Dec. 1994, On page(s): 913 - 918 vol.2 [49] L. Roberts, “Enhanced PRCA (Proportional Rate Control Algorithm)”, ATM-Forum Contribution 94-0735, Aug. 1994 [50] F.M. Chiussi, Y.T. Xia, and V.P.Kumar, “Dynamic Max Rate Control Algorithm for Available Bit Rate Service in ATM Networks”, Proc. GLOBECOM ’96, pp. 2108-2117, Nov. 1996 [51] Arulambalam, A. Xiaoqiang Chen Ansari, N., Allocating fair rates for available bit rate service in ATM networks, IEEE Communications Magazine, Volume: 34, Issue: 11, Nov 1996, On page(s): 92-100 [52] Moh, W.M. Hegde, M., Evaluation of ABR congestion control protocols for ATM LAN and WAN, International Conference on Network Protocols, 1996, 10/29/1996 -11/01/1996, Columbus, OH , USA, pp. 199-206 [53] Zukerman, M. Tse, P.W., Performance and fairness comparison between EFCI and ERF flow control schemes for the ABR service in ATM networks, 1997 IEEE International Conference on Communications, ICC 97, 06/08/1997 -06/12/1997, Montreal, Que. , Canada, Volume: 1, pp. 220-224 [54] "ERICA+: Extensions to the ERICA Switch Algorithm," ATM Forum/95-1346, October 1995, http://www.cis.ohio-state.edu/~jain/atmf/af_erc22.htm [55] D. Bertsekas, R. Gallager. Data Networks. Second edition. Prentice-Hall, Englewood Cliffs, New Jersey, 1992. Section 6.5.2. [56] Jeremy Ping Chew Anil Kumar Gupta, Active fairness: improved fairness with dynamic VC weights and generalized fairness in the ATM ABR service, IEEE Proceedings: ATM Workshop, 1999, 05/24/1999 -05/27/1999, Kochi , Japan, On page(s): 175-180

82

[57] Randhawa, T.S.; Hardy, R.H.S., Estimation and prediction of VBR traffic in high-speed networks using LMS filters. International Conference on Communications, 1998 (ICC 98). Vol. 1, pp. 253 – 258. [58] Randhawa, T.S.; Hardy, R.H.S., Application of AR based model in proactive management of VBR traffic, 1998 1st IEEE International Conference on ATM (ICATM-98), pp. 234 – 241. [59] Bin Qiu; Liren Zhang; Wu, H.R. Fuzzy multi-step ahead prediction of VBR video sources. 1997, International Conference on Information, Communications and Signal Processing, 1997 (ICICS). Vol. 3, pp. 1623 – 1626. [60] Qixiang Pang; Shiduan Cheng; Peng Zhang. Adaptive fuzzy traffic predictor and its applications in ATM networks, 1998, IEEE International Conference on Communications, 1998 (ICC 98). Vol. 3, pp. 1759 – 1763. [61] Kolarov, A. Ramamurthy, G., A control-theoretic approach to the design of an explicit rate controller for ABR service, IEEE/ACM Transactions on Networking, Oct 1999, On page(s): 741-753 [62] Laberteaux, K.P.; Rohrs, C.E.; Antsaklis, P.J., A pragmatic controller for explicit rate congestion control, IEEE Global Telecommunications Conference, 2001, GLOBECOM '01., 25-29 Nov. 2001, Volume: 4, Pages:2400 – 2406 [63] E. Altman, F. Baccelli, J-C. Bolot, ioDiscrete-time analysis of adaptive rate control mechanisms,li Intl. Conf. Data Comm Sys and Performance, Raleigh, NC, Oct. 1993, pp. 121-140 [64] E. Altman, T. Basar, and R. Srikant, irRobust rate control for ABR sources, IEEE INFOCOM 1998, vol. 1, Mar 29-Apr 2 1998, pp. 166-173 [65] Rosenberg, S.; Aissaoui, M.; Galway, K.; Giroux, N. , Functionality at the edge: designing scalable multiservice ATM networks, IEEE Communications Magazine, May 1998, Vol. 36, pp. 88 - 90, 95-99. [66] Odlyzko, A., Paris Metro pricing: the minimalist differentiated services solution, Seventh International Workshop on Quality of Service, 1999, IWQoS '99, 31 May-4 June 1999, London, UK, On page(s): 159 – 161 [67] Xinjie Chang Petr, D.W., Pricing for the multi-service networks, 2001 IEEE Workshop on High Performance Switching and Routing, 05/29/2001 -05/31/2001, 2001, Dallas, TX , USA, On page(s): 103-108 [68] Cerda, L. Casals, O., Dynamic and static charging of the ABR service in ATM Network, IEEE Global Telecommunications Conference, 1998, GLOBECOM 98, 11/08/1998 -11/12/1998, Sydney,NSW , Australia, Volume: 5, pp. 3104-3109

83

[69] Peha, J.M., Dynamic pricing as congestion control in ATM networks, IEEE Global Telecommunications Conference, 1997, GLOBECOM '97., 11/03/1997 -11/08/1997, Phoenix, AZ , USA, Volume: 3, pp. 1367-1372 [70] Fitkov-Norris, E.D. Khanifar, A., Dynamic pricing in mobile communication systems, First International Conference on 3G Mobile Communication Technologies, 2000, (IEE Conf. Publ. No. 471), 03/27/2000 -03/29/2000, 2000, London , UK, pp. 416-420 [71] J. K. Mackie-Mason, H. R. Varian, “Pricing the Internet”, Prentice-Hall,1994 [72] J. M. Peha,, “Dynamic Pricing as Congestion Control in ATM Networks”, IEEE Globecom-97, Phoenix, AZ, USA, Nov 1997 [73] H. Ji, J. Y. Hui, E. Karasan, “GoS-Based Pricing and Resource Allocation for Multimedia Broadband Networks”, IEEE Infocom’96, pp. 1020-1027, 1996 [74] V. Sharma, Reliable Estimation via Simulation, Queueing Systems, Volume 30, 1998, pp. 341-363 [75] Xinjie Chang; Petr, D.W., “Pricing for the multi-service networks”, 2001 IEEE Workshop on High Performance Switching and Routing, , 29-31 May 2001, Pages:103 – 108 [76] A. M. Odlyzko, Paris Metro Pricing for the Internet, Proceedings of the ACM Conference on Electronic Commerce, 1999, pp. 140-147 [77] S. Shenker, D. Clark, D. Estin, S. Herzog, “Pricing in Computer Networks: Reshaping the Research Agenta”, ACM Computer Communications Review, vol. 26, No. 2, April 1996 [78] A. Gupta, D.O. Stahl, A.B. Whinston, Priority Pricing of Integrated Services Networks, Internet Economics, MIT Press, 1998, pp. 323-378 [79] R. Cocchi, S. Shenker, D. Estrin, L. Zhang, Pricing in Computer Networks: Motivation, Formulation and Example, ACM/IEEE Transactions on Networks, Volume 1, 1993. [80] R. Cocchi, S. Shenker, D. Estrin and L. Zhang, A Study of Priority Pricing in Multiple Service Class Networks, Proc. of SIGCOMM, Oxford University Press, September 1991 [81] D. D. Clark, Internet Cost Allocation and Pricing, Internet Economics, MIT Press, Cambridge, Massachusetts, pp. 216-252, 1997.

84

[82] J. MacKie-Mason, L. Murphy, J. Murphy, Responsive Pricing in the Internet, Internet Economics, MIT Press, pp. 279-303, 1997 [83] Odlyzko, A., “Should flat-rate Internet pricing continue”, IT Professional , Volume: 2 , Issue: 5 , Sept.-Oct. 2000, Pages:48 – 51 [84] M. L. Honig, K. Steiglitz, “Usaged-based Pricing for Packet Data Generated by a Heterogeneous User Population”, IEEE Infocom’95, Vol.2, Boston, MA, 1995 [85] Parris, C. and Ferrari, D., “A Resource Based pricing policy for Real-time channels in a Packet-Switching Network”, Technical Report, Tenet Group, ICSI, UC Berkeley,1994 [86] Elovici, Y. Ben-Shimol, Y. Shabtai, A., Per-packet pricing scheme for IP networks, 10th International Conference on Telecommunications, ICT 2003, 23 Feb.-1 March 2003, On page(s): 1494 - 1500 vol.2 [87] Zadeh, L.A., The roles of soft computing and fuzzy logic in the conception, design and deployment of intelligent systems, Proceedings of the Sixth IEEE International Conference on Fuzzy Systems, 07/01/1997 -07/05/1997, Barcelona , Spain, Volume: 1, p. 1. [88] Zadeh, L.A., Soft computing and fuzzy logic, IEEE Software, Volume: 11, Issue: 6, Nov 1994, pp. 48-56 [89] S. Haykin, Neural Networks – A Comprehensive Foundation, Macmillan Publishing Company, 113 Sylvan Avenue, Englewood Cliffs, NJ 07632, 1994 [90] J.-S. R. Jang, C.-T. Sun, E. Mizutani. Neuro-Fuzzy and Soft Computing - A Computational Approach to Learning and Machine Intelligence, Prentice Hall, Upper Saddle River, NJ 07458., 1997 [91] L.A. Zadeh, Fuzzy Sets, Information and Control, June 1965, pp. 338-353 [92] Bonissone, P.P. Yu-To Chen Goebel, K. Khedkar, P.S., Hybrid soft computing systems: industrial and commercial applications, Proceedings of the IEEE, Volume: 87, Issue: 9, Sep 1999, pp. 1641-1667 [93] W. S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” Bull Math Biophys., Volume 5, pp.115–133, 1943. [94] F. Rosenblatt, “The perceptron, a perceiving and recognizing automaton,” Cornell Aeronautical Lab. Rep., Buffalo, NY, Project PARA, no. 85-640-1, 1957.

85

[95] F. Rosenblatt, Principle of Neurodynamics: Perceptron and the Theory of Brain Mechanisms. Washington, DC: Spartan, 1962. [96] M. Minsky and S. Papert, Perceptrons. Boston, MA: MIT Press, 1969. [97] P. Werbos, “Beyond regression: New Tools for predictions and analysis in the behavioral science,” Ph.D. dissertation, Harvard Univ., Cambridge, MA, 1974. [98] D. Parker, “Learning logic,” Center for Computational Res. Econ. Manage. Sci., Massachusetts Inst. Technol., Cambridge, MA, Tech. Rep. TR-47, 1985. [99] Y. LeCun, “Une procedure d’apprentissage pour reseau a seuil symetrique,” Cognitiva, vol. 85. Paris, France: CESTA, 1985, pp. 599–604. [100] Kohonen, T., The self-organizing map, Proceedings of the IEEE, Sept. 1990, Volume: 78 , Issue: 9, On page(s): 1464 – 1480 [101] T. Kohonen, “Self-organized formation of topologically correct feature maps,” Bio. Cybern., vol. 43, pp. 59–69, 1982. [102] J. Hopfield, “Neural networks and physical systems with emer-gent collective computational abilities,” in Proc. Acad. Sci., vol. 79, pp. 2554–2558, 1982. [103] A. Carpenter and S. Grossberg, “A Massively parallel architecture for a self-organizing neural pattern recognition machine,” Comput., Vision, Graphics, Image Processing, vol. 37, pp. 54–115, 1983. [104] J.H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, Mich., 1975 [105] J. S. R. Jang, “ANFIS: Adaptive-network-based-fuzzy inference system,” IEEE Trans. Syst., Man, Cybern., vol. 23, pp. 665–685, May–June 1993. [106] Jang, J.-S.R.; Chuen-Tsai Sun;, Neuro-fuzzy modeling and control, Proceedings of the IEEE, Volume: 83 , Issue: 3 , March 1995 , Pages:378 - 406 [107] O. Cordon, H. Herrera, and M. Lozano, “A classified review on the combination fuzzy logic-genetic algorithms bibliography,” Dep. Comput. Sci. AI, Univ. Granada, Granada, Spain, Tech. Report 95129, 1995. [Online]. Available WWW: http://decsai.ugr.s/˜herrera/flga.html. [108] D. J. Montana and L. Davis, “Training feedforward neural networks using genetic algorithms,” in Proc. 11th Int. Joint Conf. Artificial Intelligence (IJCAI-89), vol. 1. San Francisco, CA: Morgan Kaufmann, pp. 762–767, 1989.

86

[109] H. Kitano, “Empirical studies on the speed of convergence of neural network training using genetic algorithms,” in Proc. 8th National Conf. Artificial Intelligence (AAAI-90), vol. 2, 1990, pp. 789–795. [110] M. McInerney and A. P. Dhawan, “Use of genetic algorithms with backpropagation in training of feedforward neural networks,” in Proc. 1993 IEEE Int. Conf. Neural Networks (ICNN ’93), vol. 1, San Francisco, CA, 1993, pp. 203–208. [111] S. Harp, T. Samad, and A. Guha, “Toward the genetic synthesis of neural networks,” in Proc. 3rd Int. Conf. Genetic Algorithms, 1989, pp. 360–369. [112] E. Alba, J. F. Aldana, and J. M. Troya, “Genetic algorithms as heuristics for optimizing ANN design,” in Proc. Int. Conf. Artificial Neural Nets and Genetic Algorithms (ANNGA’93), Innsbruck, Austria, 1993, pp. 683–689. [113] R. A. Jacobs, “Increased rates of convergence through learning rate adaptation,” Neural Networks, vol. 1, pp. 295–307, 1988. [114] P. Arabshahi, J. J. Choi, R. J. Marks, and T. P. Caudell, “Fuzzy control of backpropagation,” in Proc. 1st IEEE Int. Conf. Fuzzy Systems (FUZZ-IEEE’92), San Diego, CA, 1992, pp. 967–972. [115] Dote, Y. Ovaska, S.J., Industrial applications of soft computing: a review, Proceedings of the IEEE, Volume: 89, Issue: 9, Sep 2001, On page(s): 1243-1265 [116] Neves, J.E. Leitao, M.J. Almeida, L.B., Neural networks in B-ISDN flow control: ATM traffic prediction or network modeling?, IEEE Communications Magazine, Volume: 33, Issue: 10, 1995, pp. 50-56 [117] A. A. Tarraf, I. W. Habib, and T. N. Saadawi, “A novel neural network traffic enforcement mechanism for ATM networks,” IEEE J. Select. Areas Commun., vol. 12, pp. 1089–1096, Aug. 1994. [118] Song Chong, San-qi Li, Ghosh, J., Predictive dynamic bandwidth allocation for efficient transport of real-time VBR video over ATM, IEEE Journal on Selected Areas in Communications, Volume: 13, Issue: 1, Jan 1995, pp. 12-23 [119] Mehrvar, H.R. Soleymani, M.R., Packet loss rate prediction using a universal indicator of traffic, IEEE International Conference on Communications, ICC 2001, 11-14 June 2001, Helsinki, Finland, Volume 3, On page(s): 647 - 653 [120] Lingfen Sun Ifeachor, E.C , Perceived speech quality prediction for voice over IP-based networks, IEEE International Conference on Communications, ICC 2002, 28 April-2 May 2002, New York, NY, USA, Volume: 4, On page(s): 2573 - 2577

87

[121] Wang, Q.P. Tan, D.L. Ning Xi Wang, Y.C., The control oriented QoS: analysis and prediction, IEEE International Conference on Robotics and Automation, ICRA 2001, On page(s): 1897 - 1902 vol.2 [122] Yau-Hwang Kuo; Mong-Fong Horng; Jung-Hsien Chiang;, An adaptive fuzzy traffic estimator of reservation protocols on IP-based networks, IEEE International Conference on Systems, Man, and Cybernetics, 1999, IEEE SMC '99, 12-15 Oct. 1999, Pages:337 - 342 vol.1 [123] Ge Ling Wu Xinyu , Intelligent prediction and smoothing of video flow for multimedia communication, 1998 International Conference on Communication Technology Proceedings, ICCT '98, 22-24 Oct. 1998, Beijing China, On page(s): 539 - 543 vol.1 [124] Bin Qiu Liren Zhang Wu, H.R., Fuzzy multi-step ahead prediction of VBR video sources, Proceedings of 1997 International Conference on Communications and Signal Processing, ICICS 1997, 9-12 Sept. 1997, Singapore, On page(s): 1623 - 1626 vol.3 [125] Qiu, B., The application of fuzzy prediction for the improvement of QoS performance, IEEE International Conference on Communications, ICC ’98, 7-11 June 1998, Atlanta, GA USA, On page(s): 1769 - 1773 vol.3 [126] Qixiang Pang Shiduan Cheng Peng Zhang, Adaptive fuzzy traffic predictor and its applications in ATM networks, IEEE International Conference on Communications, 1998, ICC 98, 7-11 Jun 1998, Atlanta, GA , USA, On page(s): 1759-1763 vol.3 [127] Mohamed, S. Cervantes-Perez, F. Afifi, H., Integrating networks measurements and speech quality subjective scores for control purposes, Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2001, 22-26 April 2001, Anchorage, AK, USA, Volume 2, On page(s): 641 – 649 [128] Ray-Guang Cheng Chung-Ju Chang Li-Fong Lin , A QoS-provisioning neural fuzzy connection admission controller for multimedia high-speed networks, IEEE/ACM Transactions on Networking, Feb. 1999, Volume: 7 , Issue: 1, On page(s): 111 - 121 [129] Runtong Zhang; Jian Ma;, Fuzzy QoS management in Diff-Serv networks, 2000 IEEE International Conference on Systems, Man, and Cybernetics, 8-11 Oct. 2000, Pages:3752 - 3757 vol.5 [130] Yager, R.R., Fuzzy methods in e-commerce, 18th International Conference of the North American Fuzzy Information Processing Society, NAFIPS 1999, 10-12 June 1999, New York, NY USA, On page(s): 5 - 11

88

[131] Tarraf, A.A. Habib, I.W. Saadawi, T.N. Ahmed, S.A., ATM multimedia traffic prediction using neural networks, Proceedings of Global Data Networking, 1993. Meeting Date: 12/12/1993 -12/15/1993, Cairo , Egypt, pp: 77-84 [132] Bor-Sen Chen, Sen-Chueh Peng, Ku-Chen Wang, Traffic modeling, prediction, and congestion control for high-speed networks: a fuzzy AR approach, IEEE Transactions on Fuzzy Systems, Oct 2000, Volume: 8, Issue: 5, pp. 491-508 [133] Aussem, A. Mahul, A. Marie, R., Queueing network modelling with distributed neural networks service quality estimation in B-ISDN networks, Proceedings of the IEEE INNS-ENNS International Joint Conference on Neural Networks, 2000. IJCNN 2000, 24-27 July 2000, Como, Italy, Volume 5, pp. 392 – 397 [134] Scheffer, M.F. Kunicki, J.S., Fuzzy logic traffic management for ATM networks, IEEE AFRICON 1996, 24-27 Sept. 1996, Stellenbosch South Africa, On page(s): 124 - 129 vol.1 [135] Wong, Y.F. Wong, W.C., A fuzzy-decision-based routing protocol for mobile ad hoc networks, 10th IEEE International Conference on Networks, ICON 2002, 2002, On page(s): 317- 322 [136] Aboelela, E. Douligeris, C., Routing in multimetric networks using a fuzzy link cost, Second IEEE Symposium on Computers and Communications, 07/01/1997 -07/03/1997, Alexandria , Egypt, pp. 397-401 [137] Chemouil, P. Khalfet, J. Lebourges, M., A fuzzy control approach for adaptive traffic routing, IEEE Communications Magazine, Volume: 33, Issue: 7, July 1995, pp. 70-76 [138] Roy, A. Banerjee, N. Das, S.K., An efficient multi-objective QoS-routing algorithm for wireless multicasting, IEEE 55th Vehicular Technology Conference, VTC Spring 2002, 6-9 May 2002, Birmingham, AL, USA, Volume: 3, On page(s): 1160 – 1164 [139] Banerjee, N. Das, S.K., MODeRN: multicast on-demand QoS-based routing in wireless networks, IEEE VTS 53rd Spring Vehicular Technology Conference, 2001. VTC 2001, 6-9 May 2001, Volume 3, On page(s): 2167 - 2171 [140] Banerjee, N. Das, S.K., Fast determination of QoS-based multicast routes in wireless networks using genetic algorithm, IEEE International Conference on Communications, ICC 2001, 11-14 June 2001, Helsinki, Finland, Volume 8, On page(s): 2588 - 2592

89

[141] Al-Qahtani, T.A. Abedin, M.J. Ahson, S.I. , Dynamic routing in homogenous ATM networks using genetic algorithms, The 1998 IEEE International Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, : 4-9 May 1998, Anchorage, AK USA, On page(s): 114 - 119 [142] Sterritt, R. Bustard, D.W., Fusing hard and soft computing for fault management in telecommunications systems, IEEE Transactions on Applications and Systems, Man and Cybernetics, Part C, May 2002, Volume: 32 Issue: 2, On page(s): 92 – 98 [143] Yang Xiao Chen, C.L.P. Yan Wang, A near optimal call admission control with genetic algorithm multimedia services in wireless/mobile networks, National Aerospace and Electronics Conference, 2000, NAECON 2000, 10-12 Oct. 2000, Dayton, OH, USA, pp. 787 – 792 [144] K. Uehara, K.Hirohito, Fuzzy Connection Admission Control for ATM Networks Based on Possibility Distribution of Cell Loss Ratio, IEEE JSAC, Volume 15, No. 2, February 1997 [145] Hiam Hiok Lim Bin Qiu, A predictive measurement-based fuzzy logic connection admission control, IEEE International Conference on Communications, 2001. ICC 2001. 11-14 June 2001, Helsinki, Finland, Volume 3, On page(s): 920 - 924 [146] You-Chang Ko Sun-Chun Park Chong-Youn Chun Hyong-Woo Lee Choong-Ho Cho, An adaptive QoS provisioning distributed call admission using fuzzy logic control IEEE International Conference on Communications, ICC 2001, 11-14 June 2001, Helsinki, Finland, Volume 2, On page(s): 356 – 360 [147] Sherif, M.R. Habib, I.W. Nagshineh, M. Kermani, P., Adaptive allocation of resources and call admission control for wireless ATM using genetic algorithms, IEEE Journal on Selected Areas in Communications, Volume: 18 Issue: 2, Feb. 2000, On page(s): 268 - 282 [148] Chang, C.J. Lin, L.F. Lin, S.Y. Cheng, R.G., Power-spectrum-based neural-net connection admission control for multimedia networks, IEE Proceedings- Communications, April 2002, Volume: 149 Issue: 2, On page(s): 70 - 76 [149] Shie-Jue Lee, Chun-Liang Hou, A neural-fuzzy system for congestion control in ATM networks, IEEE Transactions on Systems, Man and Cybernetics, Part B, February 2000, Volume: 30, Issue 1, pp. 2-9. [150] Seok-Yong Oh Dong-Jo Park , “Predictive fuzzy explicit rate allocation (PFERA) for traffic control in ATM networks”, 1999 IEEE International Fuzzy Systems Conference Proceedings, FUZZ-IEEE '99, 22-25 Aug. 1999, Seoul South Korea, On page(s): 750 - 755 vol.2

90

[151] Qiu, B. Wu, H.R., The application of fuzzy logic prediction in congestion control and its neural network implementation, IEEE Global Telecommunications Conference, 1998, GLOBECOM 98, 11/08/1998 -11/12/1998, Sydney,NSW, Australia, Volume: 4, pp. 2458-2463 [152] Kasiolas, A. Makrakis, D., A fuzzy-based traffic controller for high-speed ATM networks using realistic traffic models, IEEE International Conference on Multimedia Computing and Systems, 1999, 06/07/1999 - 06/11/1999, Florence Italy, Volume 2, On page(s): 389 - 394 [153] S.G. Jong and K. Hee, Congestion Control with Double Threshold in ATM Networks, Proceedings of GLOBECOM’94, Nov. 1994, pp. 595-599 [154] Pal, S.K. Talwar, V. Mitra, P., Web mining in soft computing framework: relevance, state of the art and future directions, IEEE Transactions on Neural Networks, Volume: 13, Issue: 5, Sept. 2002, On page(s): 1163- 1177 [155] Vasilakos, A. Ricudis, C. Anagnostakis, K. Pedryca, W. Pitsillides, A., Evolutionary-fuzzy prediction for strategic QoS routing in broadband networks, The 1998 IEEE International Conference on Fuzzy Systems Proceedings, 1998. IEEE World Congress on Computational Intelligence, 05/04/1998 -05/09/1998, Anchorage, AK , USA, Volume 2, pp. 1488-1493 [156] Rose Qingyang Hu Petr, D.W., A predictive self-tuning fuzzy-logic feedback rate Controller, IEEE/ACM Transactions on Networking, Dec 2000, Volume: 8, Issue: 6, pp. 697-709 [157] Tarraf, A.A., Habib, I.W., and Saadawi, T.N., Neural networks for ATM multimedia traffic prediction, Proceeding of the international workshop on Applications of neural networks to telecommunications, 1993, Volume 1, pp. 85-91 [158] Lin Hsiou-Ping Ouyang Yen-Chieh, Neural networks based traffic prediction for cell discarding policy, International Conference on Neural Networks,1997, 06/09/1997 -06/12/1997, Houston, TX , USA, Volume: 4, pp. 2051-2056 [159] Du, L. Bigham, J. Cuthbert, L. Parini, C. Nahi, P., Cell size and shape adjustment depending on call traffic distribution, IEEE Wireless Communications and Networking Conference, WCNC2002, 17-21 March 2002, Orlando, FL, USA, Volume 2, On page(s): 886 - 891 [160] Zomaya, A.Y. Wright, M., Observations on using genetic-algorithms for channel allocation mobile computing, IEEE Transactions on Parallel and Distributed Systems, Sept. 2002, Volume: 13 Issue: 9, On page(s): 948 – 962

91

[161] Pitsillides, A. Sekercioglu, Y.A. Ramamurthy, G., Fuzzy backward congestion notification (FBCN) congestion control in asynchronous transfer mode (ATM) networks, IEEE Global Telecommunications Conference, 1995, GLOBECOM '95, Meeting Date: 11/13/1995 -11/17/1995, Singapore, vol.1, pp. 280-285 [162] Pitsillides, A. Sekercioglu, Y.A. Ramamurthy, G., Effective control of traffic flow in ATM networks using fuzzy explicit rate marking (FERM), IEEE Journal on Selected Areas in Communications, February 1997, Volume: 15, Issue: 2, pp. 209-225. [163] Hiam Hiok Lim Bin Qiu, Performance enhancement of ERICA+ using fuzzy logic Prediction, Fifth Asia-Pacific Conference on ... and Fourth Optoelectronics and Communications, 1999, APCC/OECC '99, 10/18/1999 -10/22/1999, Beijing , China, Volume 1, pp. 134-138 [164] Chrysostomou, C. Pitsillides, A. Hadjipollas, G. Sekercioglu, A. Polycarpou, M., Fuzzy explicit marking for congestion control in differentiated services networks, Eighth IEEE International Symposium on Computers and Communication, 2003, (ISCC 2003), On page(s): 312- 319 [165] Zhang Yu Zheng Junli Chen Wenxia, Chaotic neural network method to control ATM traffic, 1998 International Conference on Communication Technology Proceedings, 1998. ICCT '98, 10/22/1998 -10/24/1998, Beijing , China, Volume 1, pp. 429-433 [166] Hall, J. Mars, P., Limitations of artificial neural networks for traffic prediction in broadband networks, IEE Proceedings on Communications, April 2000, Volume: 147 Issue: 2, pp. 114 - 118 [167] Jagannathan, S. Talluri, J., Adaptive traffic rate control of ATM networks, Proceedings of the 2000 American Control Conference, 2000, 06/28/2000 -06/30/2000, Chicago, IL, USA, Volume 3, pp. 1577-1581 [168] Jagannathan, S. Talluri, J., Congestion control of ATM networks using multilayer neural network approach: multiple source/single switch scenario, Proceedings of the 2001 American Control Conference, 2001, ACC 2001,. 06/25/2001 -06/27/2001, Arlington, VA , USA, Volume 5, On page(s): 3789-3794 [169] Jagannathan, S. Tohmaz, A., Congestion control of ATM networks using a learning Methodology, Proceedings of the 2001 IEEE International Conference on Control Applications, (CCA '01), 09/05/2001 -09/07/2001, Mexico City , Mexico, On page(s): 135-140 [170] Laberteaux, K.P. Rohrs, C.E. Antsaklis, P.J., A practical controller for explicit rate congestion control, IEEE Transactions on Automatic Control, Volume: 47, Jun 2002, On page(s): 960-978

92

[171] Runtong Zhang Jian Ma, Fuzzy QoS management in Diff-Serv networks, IEEE International Conference on Systems, Man, and Cybernetics, 2000, 10/08/2000 -10/11/2000, Nashville, TN , USA, Volume 5, pp. 3752-3757 [172] Fernandez, M.P. Pedroza, Ade.C.R. de Rezende, J.F., QoS provisioning across a DiffServ domain using policy-based management, IEEE Global Telecommunications Conference, 2001, GLOBECOM '01, 11/25/2001 -11/29/2001, San Antonio, TX , USA, Volume 4, On page(s): 2220-2224 [173] Runtong Zhang Jian Ma, Congestion control using fuzzy logic in differentiated services networks, Fourth International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 2001, 10/30/2001 -11/01/2001, Yokusika City, Japan, On page(s): 288-292 [174] Patchararungruang, S. Shenoy, N. Halgamuge, S.K., Rule based delay proportion adjustment for differentiated services, the 2002 IEEE International Conference on Fuzzy Systems, 2002. FUZZ-IEEE'02, 05/12/2002 -05/17/2002, Honolulu, HI , USA, Volume: 1, On page(s): 700-704 [175] Yaghmaee, M.H. Safavi, S.M., A new FLC based model for Differentiated Services, Seventh International Symposium on Computers and Communications, ISCC 2002, 2002, On page(s): 802- 807 [176] Kabranov, O. Tingzhou Yang Makrakis, D., Fuzzy control of pricing for bandwidth demand in obtainable forwarding service for differentiated services architecture, IEEE Telecommunications Conference 2000, GLOBECOM '02, 2002, Volume: 3, On page(s): 2175- 2179. [177] Shie-Jue Lee, Member, IEEE, and Chun-Liang Hou, A Neural-Fuzzy System for Congestion Control in ATM Networks, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 30, NO. 1, FEBRUARY 2000 [178] Qingyang Hu Petr, D.W. Braun, C., Self-tuning fuzzy traffic rate control for ATM networks, 1996 IEEE International Conference on Communications, ICC 96, 23-27 June 1996, Dallas, TX USA, On page(s): 424 - 428 vol.1 [179] Randhawa, T.S.; Hardy, R.H.S. Estimation and prediction of VBR traffic in high-speed networks using LMS filters. International Conference on Communications, 1998. ICC 98. Pages: 253 - 258 vol.1 [180] Randhawa, T.S.; Hardy, R.H.S. Application of AR based model in proactive management of VBR traffic. 1998 1st IEEE International Conference on ATM. ICATM-98. Pages: 234 - 241

93

[181] Bin Qiu; Liren Zhang; Wu, H.R. Fuzzy multi-step ahead prediction of VBR video sources. 1997 International Conference on Information, Communications and Signal Processing, 1997. ICICS. Pages: 1623 - 1626 vol.3 [182] J.-S.R. Jang, C.-T. Sun, Predicting chaotic time series with fuzzy if-then rules. Second IEEE International Conference on Fuzzy Systems 1993. Pages 1079-1084. vol 2. [183] WWW page of The Network Simulator 2 - ns-2, http://www.isi.edu/nsnam/ns/