IJCA, Vol. 11, No. 3, Sept. 2004

ISCA Copyright© 2004

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A Fuzzy-Logic Feedback Controller for ABR Traffic Management in ATM Networks

Mahir Sabra* and Mansoor Alam* The University of Toledo, Toledo, Ohio 43606-3390, USA

Abstract This paper presents a fuzzy logic feedback explicit-rate controller for the management of available bit rate (ABR) traffic in asynchronous transfer mode (ATM) networks. In our proposed flow control scheme, the switches will monitor the traffic on each link and feed periodic information to the fuzzy controller. The information is used by the controller to calculate an explicit cell rate to be sent back to the sources. We use both the queue size and the change in the queue size, besides the cell rate and the change in the cell rate as information for the calculation of the explicit rate. A complete ABR flow control scheme is presented also. This scheme is designed to achieve high link utilization with low delays, minimum queue length oscillation, fast transient response and fair rate allocation. To highlight the merit of the proposed scheme, different experimental simulations are performed. Simulation results show that the design goals are achieved, and that the performance of our algorithm is superior when compared to other control algorithms. Key Words: ATM networks, ATM traffic management, fuzzy logic control, ABR congestion control.

1 Introduction

The transfer of real time multimedia traffic has become an important issue in recent years. Broadband Integrated Services Digital Networks (B-ISDNs) provide for the integration of Multimedia services for transmitting on a transport medium using Asynchronous Transport Mode (ATM) technique. ATM has been recommended for the implementation of B-ISDNs based on its flexibility and versatility in handling Multimedia traffic. More prevalent in high-speed statistically multiplexed networks is the problem of congestion, which causes cell loss and degradation in service quality. Congestion occurs whenever the total demand is more than the total available resources of memory, links, processors, and so on. In other words, congestion happens whenever the input rate is more than the available link capacity,

* Electrical Engineering and Computer Science Department. Email: Mahir@iugaza.edu and malam@eecs. utoledo.edu.

∑ Input Rate > Available Link Capacity

Congestion is not a static resource shortage problem; rather it is a dynamic resource allocation problem. The congestion control process is concerned with allocating the resources in a network such that the network can operate at an acceptable performance level when the demand exceeds or is near the capacity of the network resources.

The organization of this paper is as follows. A Survey of ATM congestion control schemes and recent fuzzy and non-fuzzy congestion control algorithms is in Section 2. Section 3 describes the proposed ABR flow control scheme. Section 4 describes the design of the fuzzy congestion controller. Section 5 presents simulation results and compares the performance of the proposed controller with that of previous approaches. Concluding remarks are presented in Section 6.

2 Survey of ATM Congestion Control

With the growing concern of traffic and congestion control on ATM networks, many new congestion control schemes targeted to ATM networks have been proposed. Two key proposals – credit based and rate based – were discussed at length at the ATM forum. The Rate-Based feedback approach, which was eventually adopted as the standard, was first proposed by Mike Hluchyi [2] and was extensively modified later. The original proposal consisted of a rate-based version of the DECbit scheme [3], which consists of end-to-end control using a single-bit feedback from the network. A similar scheme was proposed by Newman [4]. The Explicit Forward Congestion Indication (EFCI) Scheme was proposed by M. Hluchgi. [5]. The Proportional Rate Control Algorithm (PRCA) Scheme was proposed by Barnhart [6]. This algorithm was found to have a fairness problem.

The above-mentioned schemes used single bit (binary) feedback to adjust the cell rates of the sources. The problem with this algorithm is that if resource management (RM) cells are lost due to heavy congestion in the reverse path, the sources will keep increasing their load on the forward path and eventually overload it. It was argued [7] that the binary feedback was too slow for rate-based control in high-speed networks, and that an explicit rate indication would not only be faster, but would offer more flexibility to switch designers. The ATM forum identified the explicit rate schemes to be a

IJCA, Vol. 11, No. 3, Sept. 2004 2

better class of flow control mechanisms. Anna Charny designed an explicit rate scheme called the Massachusetts Institute of Technology (MIT) scheme [8]. This proposal was well received except that the computation of fair share required order n operations, where n is the number of VCs. Search for an O(1) scheme led to the EPRCA algorithm mentioned next. The merger of PRCA with the explicit rate scheme led to the Enhanced PRCA (EPRCA) scheme at the end of July 1994 [9]. The Dynamic Max Rate Control (DMRCA) scheme [10] was developed by Chiussi, Xia and Kumar at Lucent Technologies, in an attempt to improve the EPRCA scheme. Andy Barnhart from Hughes Systems proposed another explicit rate scheme called “Congestion Avoidance using Proportional Control (CAPC)” [11]. Jain, Kalyanaraman, and Viswanathan at the Ohio Sate University (OSU) developed a series of explicit rate congestion avoidance schemes. The first scheme was called the OSU scheme [12]. A newer version of the OSU scheme, named “The Explicit Congestion Indication for Congestion Avoidance (ERICA)” algorithm, was presented at the ATM Forum in Feb 1994. In Feb 2000 Kalyanaraman, Jain, and Fahmy proposed a revised version and a consolidated description of the algorithm [13]. In February 2000, Ghani and Mark [14] proposed an enhanced rate allocation algorithm for congestion management using explicit rate feedback control. In 1999 Hac and Lin [15] presented a novel approach to ATM congestion control for ABR service. In 2000, AlHammadi and Shahsavari [16] proposed a new and improved congestion control scheme to support the ABR traffic. More detailed discussion of these algorithms and schemes is given in [39].

2.1 Fuzzy Control Schemes

Fuzzy logic has proven effective in a number of

applications, such as intelligent control and decision support, especially where a system is difficult to characterize and has strict implementation constraints. In the fields of telecommunications, network management, and queuing theory, fuzzy models have been applied and/or proposed in a number of areas. Holtzman [17] discusses the use of fuzzy approaches in the forecasting of future telecommunication services. Millstrom, Bonde, and Grimaldi [18] proposed unique hybrid architecture for automated VHF frequency management that applies fuzzy logic for enhanced signal detection and decision-making. Two applications of fuzzy modeling techniques to network troubleshooting systems are described by Lirov [19] and Lewis and Dreo [20].

The fuzzy logic technique has been used to efficiently solve several ATM problems, such as traffic enforcement [21],

traffic control [22], congestion control in ABR service [23], flow control in ABR service [24], estimation of the cell loss ratio and application to call admission control [25], and routing [26]. Also Holtzman [18] examined the use of a fuzzy approach to cope with aspects of traffic uncertainty in ATM networks. Bonde and Ghosh [27] introduced fuzzy mathematics to provide a flexible and high-performance solution to queue management in ATM networks. Ndousse [28] proposed a fuzzy logic implementation of the leaky bucket mechanism that used a channel utilization feedback via the QoS parameters to improve performance. An excellent survey of the recent fuzzy logic applications in telecommunication networks can be found in [29].

In June 1996, Cheng and Chang [30] presented a design of a fuzzy traffic controller that simultaneously manages congestion control and call admission control for ATM networks. In 1999, Cheng, Chang and Lin [31] proposed a neural fuzzy approach for connection admission control (CAC) with QoS guarantee in multimedia high-speed networks. In December 2000, Hu and Petr [32] proposed a predictive self-tuning fuzzy-logic feedback controller. In January 2001, Yaghmaee, Menhaj, and Safavi [33] presented a fuzzy logic controller (FLC) approach to control traffic sources in ATM networks. In December 2001, Alam and Jeyachandran [34] presented a fuzzy algorithm for ABR flow control in an ATM network.

3 The Proposed ABR Flow Control Scheme

3.2. The System Model Figure 1 describes the basic ABR congestion control system model. It consists of a source end-system, a switch and a destination end-system. These components are connected by bi-directional links with specified bandwidth. The source algorithm is implemented in the source end systems, both persistent (greedy) sources and transient (on-off) sources were used for simulations. Persistent sources are better to show the fairness issue and the steady state performance of the algorithm, while bursty sources are better to show the transient performance of the algorithm. Figure 2 describes the switch model of the proposed algorithm. The switch algorithm with the fuzzy system is implemented in the switching /routing processor of the switch. The destination algorithm is implemented in the destination end systems.

The transmission links used are bi-directional with specified bandwidth. One of the goals of this algorithm is to maximize the utilization of these links. The other design goals are minimizing the queue length, queue length oscillation, fast

Figure 1: Basic ABR congestion control system model

Link 1 Link 2

Source Destination Switch

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Figure 2: ABR switch model

Input ports

Input Buffer

Switching/Routing Processor

Output Ports Output Buffers

q

inqN

global queue gqQ C

f

transient response and fair rate allocation. Congestion is introduced into the system by having the rate of an input link higher than the rate of the output link. 3.2 The Proposed Scheme

Our fuzzy logic congestion control scheme requires the

switches to monitor their own load and use it with information provided by the resource management (RM) cells to compute an explicit rate (ER). The explicit rate will be fed back to the sources to adjust their cell transmission rate to the value of ER. The explicit rates of all the switches along the virtual circuit (VC) will be collected and the value from the most congested link will be sent to the source. The switches will use the queue length, change in queue length, current cell rate, and change in the cell rate as the load information to be fed to the fuzzy controller and finally compute the ER.

The destination simply returns the control (RM) cells to the source, which then adjusts its rate as indicated in these cells. 3.3 Resource Management (RM) Cells

RM cell format had to be built for the flow control scheme. The complete format of the control cell (Resource Management cell) is shown in Table 1. 3.4 The Source Algorithm

The source algorithm consists of the two following

components: 1) FRM Cells Sending Algorithm: The sources start by

sending a forward resource management (FRM) cell, then an FRM cell is sent into the network every Nrm

Table 1: RM Cell Format

Field Name Type Size (bits)

Default Value

Default Set

CI integer 1 0 set

DIR integer 1 0 set

NI integer 1 0 set

VCI integer 8 0 set

CCR double 32 -1 set

ER double 32 -1 set

MCR double 32 -1 set

DUMMY integer 317 set

data cells. Nrm is a number chosen based on the speed of the network. The Nrm value of 32 is the recommended value by the Specifications [1]. The ER field is initialized to the peak cell rate (PCR) value negotiated with the network at connection setup time. The current cell rate (CCR) field is initialized to the initial cell rate (ICR) value, and the minimum cell rate (MCR) field will be set to the negotiated MCR value.

The source will send an FRM cell if more than 100 ms have elapsed since the last FRM was sent, and at least two data cells were sent. If the source has not sent an FRM cell for more than 500 ms, then it will start sending again using the lesser of the values of ICR or CCR. Figure 3 represents a flow chart explaining the FRM and data cells sending algorithm.

2) Responding to Network Feedback The control backward resource management (BRM) cells returned

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Figure 3: Flowchart for source transmission of data and FRM cells

from the network contain an explicit rate (ER). The source will use this ER value as its new CCR and will be sending cells using this new rate. The source will also check the values of the congestion notification (CN) and the no increase (NI) fields. If any of these fields is set, the source will use the ER value only if it is less than its CCR value, otherwise the source will keep its CCR value.

If (NI and CN = 0) then

CCR ← ER. If ((NI or CN =1) and (ER < CCR) then

CCR ← ER. Else

CCR ← CCR. The source will sink (destroy) the BRM cell after reading the required fields. Figure 4 represents a flowchart explaining the source response after receiving a BRM cell. 3.5 The Switch Algorithm The switch algorithm consists of measuring the current load

Figure 4: Flowchart for source response for receiving an

BRM cell level periodically, and calculating the feedback whenever a control BRM cell is received. A control time interval (T0) is to be specified for the algorithm. During this interval the switch will monitor the traffic and at the end of this interval will measure the load level in the network. Whenever an input cell (data or FRM) arrives at the switch, its VC will be set as active, and the input cell count will be incremented. At the arrival of a BRM, the switch will calculate an explicit rate (ER) value and insert it in the BRM cell. In this section we will describe the basic switch algorithm and control actions at the end of the control interval, on the arrival of an input cell, and on the arrival of a BRM. Modifications to the basic switch algorithm are mentioned in this section also.

1) On the Arrival of Input (Data or FRM) Cell: The source of the received input cell will be marked as active, and the number of input cells will be increased by one. If the input cell is an FRM, then the ACR value will be read and assigned to the CCR value of that VC. This will insure that the CCR value, which will be used in calculating the ER, is the most recent value received from the source. Figure 5 represents a flowchart explaining steps taken when receiving an input cell.

2) At the End of the Control Time Interval (T0): The switch will determine the ABR input rate by

counting the number of input cells during the control interval, and dividing the cell number by the control time interval (T0).

Input-Rate ← Number-of-Input-Cells / T0:

No Yes

BRM cell

Read ER, CN and NI

CN =1 ?

NI =1?

ER< CCR

CCR ← ER

Destroy BRM

Yes

No

No

Yes

No

Start

Yes

No

Send FRM cell Count = 0 Timer = 0

Data-Cells-No = 0

Time to send?

No

No

Send data cell Count = Count + 1

Data-Cells-No = Data-Cells-No +1

Time to send?

Timer> 100ms?

Count = Nrm? Data-Cells-

No > 2?

Yes

Yes Yes

Execute

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On receiving Input cell

Read VC’s CCR from FRM cell

Number of cells = Number of cells + 1

Mark VC (source) active

Figure 5: Flowchart for receiving input cell algorithm steps The change in the input rate will be calculated as the difference between the previous and current input rates. Delta-Cell-Rate←Previous-Input-Rate–Current-Input-Rate

The number of active sources (VCs) will be calculated. A source or VC will be considered active if at least one cell from that source was received during the control time interval (T0). A Fair-Share will be calculated by dividing the capacity (bandwidth) of the output link by the number of active VCs competing for that link. Fair-Share ← link-Bandwidth / Number-of-Active-Sources.

The Max-ER value allocated during the last control interval will be saved as last-Max-ER, and the Fair-Share value will be assigned to the new Max-ER. Figure 6 represents a flowchart explaining the steps taken at the end of the control time interval.

3) On the Arrival of a BRM Cell: The queue length will be read by the switch, and the change of the queue length (delta-queue-length) will be calculated as the difference between the previous and current queue lengths. The current queue length, the delta-queue-length, the input rate, and the delta-input-rate will be fed to the fuzzy controller along with the link capacity. The fuzzy controller will return an adjusting ratio. This ratio is multiplied by the CCR to calculate the VC’s share.

VC-Share← Ratio * CCR. The calculated ER will be the maximum value of the Fair-Share and the VC-Share. ER-Calculated ← Max (Fair-Share, VC-Share)

This calculated ER can’t exceed the value of the link capacity or the PCR.

Figure 6: Flowchart for end of control interval algorithm steps

ER-Calculated← Min(ER-Calculated, Link-Capacity, PCR)

Finally, the least of the ER value in the BRM cell and the ER-Calculated will be inserted in the BRM cell.

ER-in-BRM-Cell← Min (ER-in-BRM-Cell, ER-Calculated)

Figure 7 represents a flowchart explaining the calculation of ER after receiving a BRM.

4) Achieving Max-Min Fairness: The above algorithm is sufficient to converge to efficient operation in all cases, and to the max-min allocations in most cases. The convergence from the transient conditions to the desired operating point is rapid, often taking less than a round-trip time. However, there are cases in which the basic algorithm does not converge to max-min fair allocations. These cases were discovered when more than one bottleneck was introduced in the network. This happens only when there are some sources which are bottlenecked elsewhere upstream, and the CCR of all remaining sources is greater than the Fair-Share. If this happens, the system remains in its current state which may or may not be fair in the max-min sense.

To achieve and ensure max-min fairness, the basic algorithm is modified as follows: For Ratio < 1 + Delta, where Delta is a small fraction, we allocate the source explicit rate ER ← Max (Fair-Share, VC-Share) as in the basic algorithm. But, for Ratio ≥ 1+delta, we calculate the ER ← Max (Fair-Share, VC-Share, Last-Max-ER). Last-Max-ER is the value of the maximum ER allocation during the last control interval. This way, we attempt to make all the rate allocations equal.

Figu

At the end of control time

Calculate number of active sources

Previous-Input-Rate = Current-Input-Rate

number of active sources = 0 number of input cells = 0

Last-Max-ER = Max-ER Max-ER = Fair-Share

Current-Input-rate = number of input cells / control Interval (T0) Delta-cell-rate = Previous-Input-Rate - Current-Input-Rate

Fair-Share = Link-capacity / number of active sources

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Figure 7: Flowchart for receiving a BRM cell and calculating ER

Fairness can be achieved only by giving the contending sources equal rates. This solution attempts to give the sources equal allocations during underload (Ratio ≥ 1+Delta), and then multiplying the equal CCRs by the same ratio during the subsequent overload (Ratio < 1+Delta) to bring them to their max-min Fair-Shares. The aim of introducing the quantity Delta is to force the allocation of equal rates when the overload is fluctuating around unity, thus avoiding unnecessary rate oscillations.

5) Avoiding Transient Overloads and Queue Length Spikes: It is possible that a source which receives feedback first can keep getting rate increase indications, purely because it sends more RM cells before competing sources can receive feedback. This results in unnecessary spikes (sudden increases) in rates and

queues. This problem can be solved by incorporating the following change to the basic algorithm. When the source is increasing from a CCR below the Fair-Share, and when either the ER-Calculated is greater than the Fair-Share value, or the queue length is above a specific threshold value, then the increase in the source rate is limited to the Fair-Share.

If (((ER-Calculated ≥ Fair-Share) and (Queue-Length ≥ Q0)) or ((CCR< Fair-Share) and (ER-Calculated ≥ Fair-Share))) Then ER-Calculated ← Fair-Share. Q0 is the threshold value for the queue length.

3.6 The Destination Algorithm The destination is simply sinking (receiving ) the data cells, and returning the FRM cells to the source. The direction bit (DIR) is changed to 1 to indicate that the RM cell is a backward resource management (BRM) cell.

4 Fuzzy Congestion Controller

The proposed system follows the general structure of a fuzzy logic controller shown in Figure 8 [35]. This system consists of Fuzzifier, Rule-Base, Inference Engine, and Defuzzifier. The description of each component of the fuzzy controller and its relevance to calculating the rate adjusting ratio, and controlling the ABR traffic is provided below. This fuzzy controller will be inside the controller module of the switch shown in Figure 1. The Ratio calculated by the fuzzy controller, which is the crisp output of the defuzzifier will be multiplied by the current cell rate (CCR) and fed back to the sources as the new Explicit Rate (ER). 4.1 Fuzzifier The fuzzifier is used to convert the crisp inputs provided to the system into fuzzy membership values to be used by the inference engine. The crisp inputs measured and calculated by the algorithm are queue length, delta queue length, input rate, and delta input rate. These inputs are fed to the fuzzy controller as crisp inputs, where the fuzzifier will convert them into fuzzy values. Tables 2 and 3 show the inputs and the output used in the proposed fuzzy system and their fuzzy sets.

Figure 8: Fuzzy logic controller

Fuzzy Inputs Fuzzy Outputs

Inference Engine

Rule Base

Fuzzifier

Crisp Inputs

Defuzzifier

Crisp Outputs

On BRM cell

Read queue length Delta-queue-length = previous-queue-

length Step 1

Calculate rate adjusting Ratio using the fuzzy controller

VC-Share = Ratio * CCR

ER = Min (ER, Link-capacity, PCR)

ER in RM cell = Min (ER in RM cell,

Insert ER in the BRM cell

Step 2

Step 3

Step 5

Step 6

Step 7

ER = Max (Fair-Share, VC-Share) Step 4

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Table 2: Inputs and their fuzzy sets Inputs Fuzzy Sets

• Queue length {Short, Medium (Med), Long}

• Change in queue length {Positive (Pos), Negative (Neg)}

• Input cell rate {High, Good, Low} • Change in input cell

rate {Less, More}

Table 3: Output and its fuzzy set

Output Fuzzy Set

Cell Rate Adjusting Ratio

{Increase More (IM), Increase Less (IL), Decrease More (DM), Decrease Less (DL)}

The trapezoidal membership function was used to map the crisp inputs to their fuzzy membership values. Figures 9, 10, 11, 12 and 13 describe the membership functions and the Universe of Discourse of the input and output fuzzy sets.

Figure 9: Delta input cell rate – membership functions

Figure 10: Queue lengths – membership functions 4.2 Rule Base A rule base is a composition of IF-THEN statements which describe the actions to be taken under specified conditions. The rule base for our proposed controller has been designed

Figure 11: Delta queue lengths – membership functions

Figure 12: Input cell rate – membership functions

Figure 13: Cell rate adjusting ratio – membership functions based on the reaction of an expert human operator to a particular situation in an ATM network. The knowledge and experience of such operator is gained through the extensive study and observation of real networks and the understanding of the ABR congestion scheme in ATM networks. The design process has also taken into account the goals of designing our ABR congestion control scheme. Among these goals are allocation fairness, maximizing bandwidth utilization, and avoiding VC starvation. The rule base for the algorithm is presented in Table 4. The rule base uses the queue length, change in the queue length, the current cell rate, and the change in the cell rate to calculate the output of each rule. These outputs are then used to calculate the value of ratio which is used to adjust and calculate the Explicit Rate (ER). 4.3 Inference Engine The inference engine arrives at the fuzzy control value using

Negative µN (∆Q)

Membership Value

-100 -97 -3 0 5 97 100

Delta Queue Length

Positive µP (∆Q)

-3 0 5

Delta Input Cell Rate

1.0

Less µL (∆R)

Membership Value

-100 -97

More µM (∆R)

97 100

Good µG (R)

Low µL (R)

Membership Value

Cap – 5000 Cap -3000

Input Cell Rate

High µH (R)

1.0

Cap Cap + 3000

Membership Value

1.0 µDM µIM µDL µIL

0.75 0.9 1.1 1.25

Rate adjusting Ratio

Membership Value

Queue Length

1.0

Med µM (Q)

Short µS (Q)

13 18 22 25 100

Long µL (Q)

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OUTPUTINPUTS Table 4: Fuzzy Rule Base

Q = queue length, ∆Q = change in the queue length, Neg = Negative, Pos = Positive, Med = Medium, IL = Increase Less, IM = Increase More, DL = Decrease Less, DM = Decrease More.

the membership values of the inputs and the rules mentioned in the rule base. The inference strategy proposed and used in our fuzzy controller is the Max-Min inference method [36]. In this method the firing strength for each output set is calculated as the minimum of the membership values of the corresponding input sets. The final output for each output set is the maximum of the individual firing strengths obtained from each rule for that output set. 4.4 Defuzzifier The defuzzifier takes in the fuzzy output values and converts them into crisp output control values. The defuzzification strategy used in our proposed controller is the Mean of Maximum Method (MOM) [36], which takes the maximum of the membership values of each output set fired by each rule and calculates the crisp output as the ratio of the weighted sum of the centriods of the outputs to the sum of the weights. The cell rate adjusting ratio which is used to calculate the explicit rate (ER) is derived as follows:

DMDLILIM

DMcDLcILcIMcwwww

wDMwDLwILwIMRatio

+++×+×+×+×

=

where,

MIN-MAX theof outputs theare and ,,, DMDLILIM wwww

.7.0,95.0,1.1,25.1 and engine inference == cccc MDLILIM Detailed explanation and examples of how to use this equation to calculate the Ratio are given in [39].

5 Simulation Results The simulation package used in this study is the OPNET Modeler by OPNET Technologies, Inc. [37]. OPNET provides a comprehensive environment supporting the modeling and performance evaluation of communication networks and distributed systems. The metrics used to study the performance of the algorithm and compare it to other algorithms are those which define the objectives of the ABR service in the ATM networks. These metrics were link utilizations, individual channel throughputs, cell loss, and buffer occupancies. OPNET’s probe editor was used to capture this wide range of statistics on the run during the simulation process. These collected statistics were analyzed using OPNET‘s analysis tool. The performance of the algorithm is studied under different loading conditions and congestion situations. Persistent and transient sources are used, and network models with single and multiple bottlenecks are used as well. Our fuzzy algorithm performance is compared to the1994 EFCI algorithm [5], the 1994 PRCA algorithm [6], the ERICA algorithm [13] presented in February 2000, and the Jeyachandran fuzzy algorithm [34] presented in December 2001.

Rule Q ∆Q Input Rate ∆ Input Rate Ratio

1 Short Neg High Less IL

2 Short Neg High More DL

3 Short Neg Good Less IM

4 Short Neg Good More IL

5 Short Neg Low Less IM

6 Short Neg Low More IM

7 Short Pos High Less IL

8 Short Pos High More DL

9 Short Pos Good Less IM

10 Short Pos Good More IL

11 Short Pos Low Less IM

12 Short Pos Low More IM

13 Med Neg High Less IL

14 Med Neg High More DL

15 Med Neg Good Less IM

16 Med Neg Good More IL

17 Med Neg Low Less IM

18 Med Neg Low More IM

19 Med Pos High Less IL

20 Med Pos High More DM

21 Med Pos Good Less IL

22 Med Pos Good More DL

23 Med Pos Low Less IM

24 Med Pos Low More IL

25 Long Neg High Less DL

26 Long Neg High More DL

27 Long Neg Good Less IL

28 Long Neg Good More DL

29 Long Neg Low Less IM

30 Long Neg Low More IL

31 Long Pos High Less DM

32 Long Pos High More DM

33 Long Pos Good Less DL

34 Long Pos Good More DM

35 Long Pos Low Less DL

36 Long Pos Low More DM

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Figure 14: Single bottleneck network model

5.1 Simulation Parameters Values Throughout our experiments, the following parameter values were used:

1. Links have a bandwidth of 155.52 Mbps. 2. To introduce congestion in the network models, the

bandwidth of the output link (Link2) was modified to 125 Mbps.

3. All VCs are bidirectional. 4. PCR = 155 Mbps. 5. MCR = 5000 Kbps. 6. ICR = 7.75 Mbps. 7. All link distances are 100 meters. 8. Buffer capacity is 32 Kbits (77 cells). 9. Switching processing rates are 1.2 Gbps. 10. The switch averaging control interval was set to 1.0 ms. 11. All sources are deterministic, i.e., their start /stop times

and their transmission rates are known. 12. The Nrm value was set to 32. 13. The value of Delta was set to 0.1.

The network model in Figure 14 was used to simulate the performance of our algorithm for the case of one bottleneck in the network. The bottleneck was created in switch 2 by making the bandwidth of the output link less than that of the input link. 5.2 Buffer Occupancy Oscillations Erratic oscillations in the buffer occupancy levels might lead to false alarms about the severity of congestion. The fuzzy algorithm keeps the queue length low and the oscillations at minimum. This is because of the capability of the fuzzy

algorithm to use the queue length, the derivative of the queue length, besides the cell rate and the derivative of the cell rate for its decision-making purposes. Keeping the oscillations minimized is very crucial especially in the case of transient sources, where sources are constantly connecting to and leaving the network. This is the case in real world networks, where the starting and stopping of network connections is dynamic and varying constantly. Figures 15 and 16 show the queue length variations for different algorithms for both switches. Our fuzzy algorithm shows small queues length with minimum queue length oscillations. The ERICA [13] algorithm exhibits high queue length oscillations. The two algorithms with comparable queue length behavior are the EFCI [5] and the fuzzy algorithm by Jeyachandran [34]. The EFCI algorithm exhibits low link utilizations, and the other fuzzy algorithm has a fairness problem since it tends to starve the transient sources as discussed in the following two sections. 5.3 Persistent and Transient Sources We used both persistent and transient sources in our network model to study the performance of the algorithm in an environment similar to real world’s networks. In our network model source_2 and source_3 were modeled as persistent sources (i.e., they will be trying to transmit data continuously up to the allowed PCR). Source_1a is on during the time interval 0 > t > 0.2 sec, off during the interval 0.2 > t > 0.3 sec, and then on during the interval t >0.3 sec. Source_1b is off during the interval 0.0 > t > 0.1 sec, on during the interval 0.1 > t > 0.7 sec, off during the interval 0.7 > t > 1.0 sec, and on during the interval t > 1.0 sec. Source_1a and source_1b both shared the same virtual circuit (VC1). Figure 17 shows the different source utilizations for the fuzzy algorithm. The total

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Figure 15: Switch 1 output buffer queue lengths

Figure 16: Switch 2 output buffer queue lengths

Figure 17: Fuzzy source utilizations

available link bandwidth is divided equally and fairly among the sources, and there are no oscillations in the throughput of the sources. When one source stops sending, its bandwidth share will be quickly divided among other contending sources. Also, when a new source connects to the network, a fair share of the bandwidth is allocated to that source. The ERICA

algorithm performance (Figure 18) is comparable to our algorithm. The other algorithms either suffer oscillations in their source throughputs and unfair share of the bandwidth, especially in the case of transient sources as in the case of the PRCA algorithm (Figure 19), or low source utilizations as in the case of the EFCI algorithm shown in Figure 19. Figures 21– 24 show how quickly the total bandwidth is redistributed among sources in the case of our fuzzy algorithm and the

Figure 18: ERICA source utilizations

Figure 19: PRCA sorce utilizations

Figure 20: EFCI source utilization

0

70

140

210

0.0 0.2 0.4 0.6 0.7 0.9 1.1time (sec)

cells

EFCI PRCA ERICA Fuzzyv Fuzzy

0

40

80

0.0 0.4 0.8 1.2time (sec)

source_1 source_1b source_2 source_3

0

40

80

0.0 0.4 0.7 1.1time (sec)

source_1a source_1bsource_2 source_3

0

40

80

0.0 0.3 0.6 0.8 1.1time (sec)

source_1a source_1bsource_2 source_3

0

20

40

0.0 0.4 0.7 1.1time (sec)

source_1a source_1b source_2 source_3

0

50

100

150

0.0 0.2 0.4 0.6 0.7 0.9 1.1time (sec)

cells

EFCI PRCA ERICA Fuzzyv Fuzzy

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Figure 21: Source_1a utilizations

Figure 22: Source_1b utilzations

Figure 23: Source_2 utilizations

ERICA algorithm, while this is not the case for the other algorithms. Our fuzzy algorithm shows fast transient response and the source throughputs reach steady state quickly. 5.4 Fairness The total available bandwidth is expected to be divided

Figure 24: Source_3 utilizations

among all contending sources equally and in a fair manner. Some algorithms tend to starve the sources that have to go through a larger number of switches than other sources. In some other algorithms, the sources that start sending later than other sources are less fortunate in getting their fair share of the bandwidth. Our algorithm was tested in all these different cases and proved to be always fair. Figure 17 shows the performance of our algorithm. Source_1a and source_1b have a fair bandwidth share although they are using VC1 which is traveling through larger number of switches than other VCs. Figures 21 to 24 compare the performance of our algorithm to the other algorithms. The PRCA algorithm and the fuzzy algorithm proposed by Jeyachandran tend to starve source_1a and source_1b. 5.5 Link Utilizations and Throughputs Our fuzzy algorithm was able to achieve high utilization and throughput for the two links. This is due to its ability to allocate resources fairly among the contending sources and its ability to reduce missed service opportunities by keeping the buffer oscillations to a minimal level. The algorithm was tested for the utilizations and th roughputs of the links between the switches, link1 and link2. These inter-switch segments are the links for which the switches make the allocation using the fuzzy algorithm and hence were chosen to be a probe point. Link1 is shared by VCs 1 and 2, while link2 is shared between VCs 1 and 3. Figures 25 and 26 compare the utilization of link1 and link2 for our fuzzy algorithm to the other algorithms. It’s clearly shown that the fuzzy algorithm achieved high link utilization for both links, which is not the case for all the other algorithms. 5.6 Cell Loss Ratio Cells are segmented packets. Loss of one cell causes the retransmission of the entire packet, which causes a drop in the efficiency of the network. The Available Bit Rate (ABR) category was conceived for the purposes of supporting data critical and time insensitive applications. This prompted the

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proponents of the ABR category to include a sharp objective for cell loss. Our fuzzy algorithm was able to achieve a zero Cell Loss Ratio (CLR). This was the case also with the other algorithms. The CLR graphs are shown in Figure 27.

Figure 25: Link 1 utilization

Figure 26: Link 2 Utilization

Figure 27: Link 2 cell loss ratio

Figure 28: Link 2 overhead

5.7 Control Overhead The Control overhead is the percentage of the bandwidth used for the control cells (the RM cells in the case of our algorithm). Using the value of 32 for the Nrm parameter limited the overhead to the value of 6 percent. This overhead value is the recommended value by the specifications [1]. Figure 28 compares the overhead of our fuzzy algorithm to the other algorithms. The cell traffic in the backward direction of link2 was graphed as an indication of the overhead.

6 Conclusions In our paper we have presented the design and evaluation of a fuzzy logic explicit-rate controller for the management of ABR traffic in ATM networks. We presented a flow control scheme for the ABR service with source and switch models. The key design goals are high link utilization, low queuing delays with minimum queue length oscillation, fast transient response and max-min fair steady state operation. We then presented the fuzzy congestion controller. The scheme requires that the switches monitor the traffic on each link and feed periodic information to the fuzzy controller. The information is used by the controller to calculate an explicit cell rate to be sent back to the sources. We used both the queue size and change in queue size, besides the cell rate and the change in the cell rate, as information for the calculation of the explicit rate. Simulation results show that the design goals are achieved, and that the performance of our algorithm is superior when compared to other control algorithms.

References [1] ATM Forum, “The ATM Forum Traffic Management

Specification Version 4.0,” Ftp://ftp.atmforum.com/ pub/approved-specs/af-tm-0056.000.ps, April 1996.

[2] M. Hluchyi, L. Roberts, B. Makrucki, T. Tofigh, A. W.

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Mahir Sabra received his BSEE in 1989, Msc in 1991, and PhD in 2002 from the University of Toledo. From 1991-1996 he was a lecturer in the Electrical and Computer Engineering Department, at the University of Gaza. Dr. Sabra was the Computer Center Director from 1996-1998 and currently he is an Assistant Professor at the Islamic University

of Gaza. His research interests are congestion control and QoS in ATM networks, and fuzzy controllers.

Mansoor Alam is a professor of Electrical Engineering and Computer Science at The University of Toledo, Ohio. He has served as the Under-graduate Director of the Computer Science and Engineering Program and as Graduate Director, EECS Department. His research interests include fault-tolerant computing, ATM and MPLS networks, schedul-

ing algorithms in multiservice routing switches, performance analysis of high-speed networks, Internet QoS control, the integration of ATM and IP networks. He has published extensively in IEEE transactions and refereed international conferences and journals in these areas. He is the Director of the Ohio Communications and Computing ATM Research Network (OCARNet) which has been established with grants from the Ohio Board of Regents (OBOR) and the NSF equipment grant through the Washington University’s Gigabit Networking research program. He is a senior member of IEEE.