Proceedings of IEEE CCIS2011 A QoS-AWARE SYSTEM FOR MOBILE CLOUD

COMPUTING Peng Zhang1, Zheng Yan2, 3

1 Research Institute of Mobile Internet Xi’an University of Posts and Telecommunications, Xi’an, China

2 Department of Communications and Networking, Aalto University, Espoo, Finland 3 School of Telecommunications Engineering, XiDian University, Xi’an, China

pzhang@xupt.edu.cn, zheng.yan@aalto.fi

Abstract

With the rapid growth of mobile smart phone users, more and more mobile users are using mobile phones to access Internet services. Meanwhile, cloud computing is changing the landscape of Internet services, so as to affect the mobile services. Cloud computing is regarded as the future of mobile. However, cloud computing still faces a number of challenges, one of which is Quality of Services (QoS), that is, how a service provider can ensure QoS of its cloud services, especially for mobile users. In this paper, we present a QoS framework for mobile cloud computing and an adaptive QoS management process to manage QoS assurance in mobile cloud computing environment. Furthermore, we present a QoS management model based on Fuzzy Cognitive Map (FCM). With an example, we evaluate the proposed system and demonstrate its effectiveness and benefits. Keywords: QoS; Cloud computing; mobile services, Fuzzy Congnitive Map

1 Introduction

In recent years, cloud computing has been paid wide attention by both industry and academia. Cloud computing offers a number of advantages such as scalability, agility and economy efficiency, in comparison of traditional IT infrastructure [1]. It virtualizes physical and software resources and provides generic services, e.g., Infrastructure as a Service (IaaS), Software as a Service (SaaS), etc. So, it is regarded as a new paradigm and it is dramatically changing the landscape of information technologies. Meanwhile, contributed by the rapid deployment of broadband wireless networks and fast growth of smart phones, more and more users are using mobile phones to access Internet services. Cloud computing is seen as the future of mobile [2]. However, cloud computing still faces a number of challenges, one of which is Quality of Service

(QoS), i.e., how a service provider can ensure QoS for its cloud services [3]. Herein, QoS refers to a set of properties including objective ones (e.g., transmission rate, delay variance, packet loss, cost and reputation) and subjective ones (user experience, trust, privacy concern and satisfaction degree). There are some existing works on QoS assurance for cloud computing, e.g., QoS framework and various QoS mechanisms [4-9]. Especially, there still lacks a comprehensive study on QoS for mobile cloud services. Notably, mobile cloud services are often affected by many specific factors, e.g., hardware and software limitations of mobile devices, signal strength of mobile networks, mobility of mobile users, etc. Thus, providing QoS assurance for mobile cloud services requires a more advanced infrastructure and more effective mechanisms than traditional cloud services, e.g., based on Internet and personal computers. This paper presents a QoS aware system for mobile cloud services. The system provides a QoS framework to monitor the status of QoS in each mobile cloud service terminal. In particular, the system uses a number of QoS properties as key parameters to evaluate QoS. Based on the evaluation result, it adopts a suitable QoS mode to ensure the service quality at service provision and execution time. The system also considers other factors’ influence on different cloud service modes with regard to QoS, e.g., availability, stability, etc. This is generally neglected in the past work. The rest of the paper is organized as follows. Section 2 briefly reviews related work. Section 3 proposes a QoS framework and algorithms for mobile cloud service, followed by simulation evaluation in Section 4. Conclusions and future work are presented in the last section.

2 Related work

The research work on cloud computing falls in various aspects such as cloud computing architecture, middle-ware design, cloud services,

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cloud security, and resource management [3]. Among those, QoS is one of the key challenges. Some of existing work focuses on QoS-aware web service. Lodi et al [4] proposed a middle-ware architecture for enabling Service Level Agreement (SLA)-driven clustering of QoS-aware application servers. Some work focus on QoS architecture design for cloud computing. Wang et al [6] proposed an adaptive QoS management framework for VoD (Video On Demand) cloud service centers. Ye et al [7] proposed a Framework for QoS and power management in a service cloud environment with mobile devices. Some work focus on mechanisms for QoS management in cloud computing. Li [8] proposed an adaptive management of virtualized resources in cloud computing using feedback control. Xiao [9] proposed a reputation-based QoS provisioning in cloud computing via Dirichlet multinomial model. However, little existing work comprehensively and flexibly supports using both QoS properties and QoS service modes as key parameters for runtime QoS adaptive assurance. Particularly, Yan [10] proposed an adaptive trust control model to specify, evaluate, establish, and ensure the trust relationships among system entities. In this paper, we applied similar model as in [10]. But the solution in [10] especially the model adjustment is too complicated and inefficient to be applied. Thus, we simplify the model and apply it in the context of mobile cloud computing.

3 Cloud-Based QoS-Aware System

3.1 System architecture

Figure 1 QoS framework for mobile cloud computing

Figure 1 shows a QoS management framework for mobile cloud computing. In a mobile device, a QoS agent monitors QoS status at run time, e.g., percentage of memory and CPU consumption, connection speed, remaining battery percentage and packet loss rate, etc. The QoS status will be

reported to a QoS management center in cloud side. The QoS management center aggregates and analyzes the huge set of QoS data, and dynamically adjusts resources to meet QoS requirements of each mobile cloud service.

Based on the QoS management framework, we apply several modes of mobile cloud services. Each mode contains multiple services, mechanisms and resource configuration schemes. A cloud service mode is a specific configuration to guarantee the QoS requirements for a cloud service. Notably, the mobile cloud computing platform can provide multiple similarly functioned services that can satisfy the demand of an integrated service. Especially, the QoS requirements of a service can be assured by selecting suitable service modes.

3.2 QoS management process

Figure 2 Adaptive QoS management process

We propose a self adaptive QoS management process for mobile cloud services as shown in Figure 2. In this process, QoS Predication is a mechanism to predict performance of a set of cloud service modes before selecting a service mode. Mode selection is a mechanism to select the best service mode based on previous prediction results. QoS Assessment is a mechanism to

QoS prediction

Mode selection of cloud service

Find a suitable mode

Optimize mode or give warming

Apply the mode

Monitor QoS status

QoS assessment is positive

Self-adapt the mode

No

No

Yes

Yes

OS

QoS agent

App App

Mobile device

Cloud services (resources)

App App

App

QoS management

QoS data collector

Data Analyzer

Resource controller

Monitor and control

monitor and assess the QoS status according to users’ QoS requirements. For the QoS requirements of a service, the QoS values can be predicted by assuming a service mode is selected. Based on prediction results, a service mode can be selected and set as system configuration. The QoS assessment mechanism evaluates the QoS status by monitoring the performance of the cloud service. According to the assessment results, the system adjusts the parameters of QoS control model to reflect real status. The adjustment happens when the evaluation result is below a threshold that is defined by users. The process runs over to achieve the self adaptive QoS management in the dynamic mobile cloud environment. In particular, the QoS management supports context awareness by adaptively selecting a proper set of service modes that can always ensure the quality of cloud services.

3.3 Modeling

We apply Fuzzy Cognitive Map to model the factors considered in adaptive QoS management. FCM specifies the interconnections and influences between nodes. It also permits updating the construction of the graph, such as adding or deleting an interconnection or a node [11]. FCM is a useful method in modeling and control of complex systems. It helps the system designer in decision analysis and strategic planning. Based on the FCM theory, a stable control performance could be anticipated based on a specific FCM configuration. Thus, we can utilize it to predict the performance of cloud service modes in order to select the best one.

Figure 3 QoS management modeling based on FCM

We propose a QoS management model with FCM as illustrated in Figure 3. The model includes three layers of nodes. The top layer node represents QoS values of a cloud service or an integrated set of cloud services. The middle layer includes the QoS parameters ( )niQAi ,...,1= of the service. The bottom layer includes cloud service modes

( )mjC j ,...,1= . Each cloud service mode has impact on QoS parameters and therefore on QoS value. On the other hand, the QoS value also has impact on the effectiveness of each service mode. Here,

we assume that each service mode is independent from each other.

The value of each node is influenced by the values of its connected nodes with appropriate weights and its previous value. Thereby, we apply an addition operation to account for both. The QoS value can be described as:

⎟⎠

⎞⎜⎝

⎛+= ∑

=

oldn

iQAi QVwfQ

i1

(1)

, such that 11

=∑=

n

iiw , where iw is the weight

indicating the importance rate of the QoS attribute

iQA regarding how much this attribute is considered at the QoS assessment. The iw can be set based on the user’s criteria (in practice can be selected from a profile).

iQAV is the value of the

QoS attribute and oldQ is the respective values of Q in the previous iteration.

iQAV can be calculated according to the following formula:

⎟⎟⎠

⎞⎜⎜⎝

⎛+= ∑

=

m

j

oldQACCjiQA ijji

VBVcwfV1

(2)

, where jicw is the influence factor of service mode

jC on iQA , jicw is set based on the impact of jC on iQA . A positive jicw indicates a positive influence of jC on iQA . A negative jicw implies a negative influence of jC on iQA . CjB is the selection factor of jC , which can be either 1 if jC is applied or 0 if jC is not applied. Notably,

jCB indicates the current cloud computing platform configuration regarding which service mode is applied for a cloud service. The value of the service mode can be calculated using

( )oldCCC jjj

VBQfV +⋅= (3)

We apply the Sigmoid function as a threshold function: ( )xexf α−+= 1/1)( , e.g., 2=α to map node values QVV

ji CQA ,, into [0, 1]. Note that

[ ]1,0,, ∈QVVji CQA , [ ]1,0∈iw , and [ ]1,1−∈jicw .

oldQAi

V and oldC j

V are the respective values of iQAV ,

and jCV in the previous iteration.

3.4. Algorithms for QoS prediction and service mode selection

The service modes are predicted through evaluating all possible modes based on the proposed model using the prediction algorithm described in Algorithm 1. For predicting new modes, we introduce a constantδ , which is the

QoS

QA1 QA2 QAi

C1 C2 Cj

W1 W2

Wi

BC1 BC2 BCj

CW11

CW2i

CW21 CW22 CWji

CWj2

……

……

accepted QΔ that controls the iteration of the prediction.

The service mode is selected based on the service mode prediction results. We select the service mode with the prediction result that satisfies the basic QoS threshold and has the lowest cost. Thereafter, the mobile device will monitor the QoS properties at service run-time in order to decide if the QoS of currently offered service is satisfied or not by its user based on pre-contracted QoS profile agreed between the user and the mobile cloud computing provider. If the QoS is satisfied, the system keeps the current service setting. Otherwise, the system will adjust the service to a higher level profile (i.e., a service mode with better QoS supports).

Algorithm: QoS prediction Input: ),...,1( mjC j = , δ

Output: jQAi

V ,, Q

For each service mode, i.e., ),...,1( mjC j =∀ , do {

( )oldCCC jjj

VBQfV +⋅=

⎟⎟⎠

⎞⎜⎜⎝

⎛+= ∑

=

m

j

oldQACCjiQA ijji

VBVcwfV1

⎟⎠

⎞⎜⎝

⎛+= ∑

=

oldn

iQAi QVwfQ

i1

} while δ≥−=Δ oldQQQ

4 Example and simulation

In this part, we use an example based simulation to evaluate the performance of the QoS management mechanism proposed by us.

4.1 Example explanation The simulation is based on an example as shown in Figure 4. It is a multi-to-multi video conference service. The QoS properties include three variables, i.e.,

1QA transmission rate, 2QA packet loss, and

3QA cost.

There are three service modes offered by the cloud computing platform:

1C : High configuration mode with high cost

2C : Medium configuration mode with medium cost

3C : Low configuration mode with low cost

6.01 =w , 2.02 =w , and 2.03 =w

Note that three service modes ( 1C , 2C , and 3C ) and their influence factors are specified in the system’s profile, an example used in our simulation is shown in Figure 4.

Figure 4 Simulation configurations

4.2 Simulation

Figure 5 QoS value calculation with different initial QoS value

First, we run the simulation by giving different initial QoS values. As shown in Figure 5, after a few times iteration, the QoS value calculation becomes stable no matter which initial value. This shows that the QoS predication algorithm is robust irrespective of initial QoS value.

Figure 6 1QA calculation with different initial 1QA

Similarly, we run the simulation by changing the initial value of 1QA . As shown in Figure 6, we find the similar result that 1QA becomes stable after a few iteration irrespective of initial value.

QoS

Tx Rate Pkt loss

rate

Cost

C1 C2

Cj

0.6

0.5 0.2

0.2

0.5 0.5

0.1

0 0 1

0.8 0.6

0.7

0.5

0.4

0.4 0.3 0.5

0.5

5 Conclusions and future work

In this paper, we proposed an adaptive QoS management system for mobile cloud computing. This solution facilitates QoS prediction, establishment, assessment and assurance. We introduced the influence of QoS properties and service modes into the model, which supports adaptive QoS management according to QoS assessment based on runtime QoS observation. We applied the FCM theory into QoS management and showed its practical effectiveness through case study. We reported simulation-based experimental results to verify the proposed system and demonstrated its effectiveness and benefits. We contributed to a practical solution that can react against QoS unsatisfactory adaptively at service runtime and handle the QoS requests with different criteria.

Our research on adaptive QoS management for mobile cloud computing continues along in following directions: First, a remaining practical challenge is generation of a good model with suitable configurations. Second, we are working towards implementing the solution.

References

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