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
[email protected],
[email protected] (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.
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; services, Fuzzy Congnitive Map
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.
mobile
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].
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,
However, cloud computing still faces a number of challenges, one of which is Quality of Service ___________________________________ 978-1-61284-204-2/11/$26.00 ©2011 IEEE
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.
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.
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
However, little existing work comprehensively and flexibly supports using both QoS properties and QoS service modes as key parameters for runtime QoS adaptive assurance.
QoS prediction
Optimize mode or give warming
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.
Mode selection of cloud service
No Find a suitable mode Yes Apply the mode
3 Cloud-Based QoS-Aware System Monitor QoS status
3.1 System architecture QoS management
Yes AppApp
QoS agent OS
QoS data colle ctor
Data Analyzer
Ap Appp App
QoS assessment is positive
Resource controller
No
Monitor and control
Self-adapt the mode
Cloud services (resources) Mobile device
Figure 2 Adaptive QoS management process
Figure 1 QoS framework for mobile cloud computing
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
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
we assume that each service mode is independent from each other.
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.
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:
⎛ n ⎞ Q = f ⎜ ∑ wiVQAi + Q old ⎟ ⎝ i =1 ⎠ n
, such that
considered at the QoS assessment. The wi can be set based on the user’s criteria (in practice can be selected from a profile). VQAi is the value of the QoS attribute and Q
is the respective values of i
according to the following formula: ⎛ m ⎞ old ⎟ VQAi = f ⎜ ∑ cw jiVC j BC j + VQA i ⎟ ⎜ ⎝ j =1 ⎠
(2)
, where cw ji is the influence factor of service mode C j on QAi , cw ji is set based on the impact of C j
on QAi . A positive cw ji indicates a positive influence of C j on QAi . A negative cw ji implies a negative influence of C j on QAi . BCj is the selection factor of C j , which can be either 1 if C j
W1 BC2
is applied or 0 if C j is not applied. Notably, BC j
Wi
W2
QA1
BCj ……
QA2
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
QAi
(
CWj2 CW22
VC j = f Q ⋅ BC j + VColdj
CWji
C2
……
)
(3)
We apply the Sigmoid function as a threshold function: f ( x ) = 1 / 1 + e −αx , e.g., α = 2 to map into [0, 1]. Note that node values VQAi , VC j , Q
CW2i C1
old
Q in the previous iteration. VQA can be calculated
QoS
CW11
= 1 , where wi is the weight
indicating the importance rate of the QoS attribute QAi regarding how much this attribute is
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.
CW21
i
i=1
3.3 Modeling
BC1
∑w
(1)
(
Cj
Figure 3 QoS management modeling based on FCM
)
VQAi ,VC j , Q ∈ [0,1] , wi ∈ [0,1] , and cw ji ∈ [− 1,1] .
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 QAi (i = 1,..., n ) of the service. The bottom layer includes cloud service modes C j ( j = 1,..., m ) . Each cloud service mode has impact
old VQA and VColdj are the respective values of VQAi , i
and VC j 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 constant δ , which is the
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,
accepted ΔQ that controls the iteration of the prediction. 0.6
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: C j ( j = 1,..., m) , Output:
0.1
QoS
0.2
0.2
0.5
0.5 0.5
Tx Rate
Cost
Pkt loss rate
0.6
0.5 0.4
0.5 0.3
0.8
0.4
0.7 0.5
1
C1 C2
0
Cj
Figure 4 Simulation configurations
δ
4.2 Simulation
VQAi , j , Q
For each service mode, i.e.,
(
∀C j ( j = 1,..., m) , do {
VC j = f Q ⋅ BC j + VCold j
)
⎛ m ⎞ old ⎟ VQAi = f ⎜⎜ ∑ cw jiVC j BC j + VQA i ⎟ ⎝ j =1 ⎠ n ⎛ old ⎞ Q = f ⎜ ∑ wiVQAi + Q ⎟ ⎝ i =1 ⎠ } while ΔQ = Q − Q old ≥ δ Figure 5 QoS value calculation with different initial QoS value
4 Example and simulation
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.
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., QA1 transmission rate, QA2 packet loss, and QA3 cost. There are three service modes offered by the cloud computing platform: C1 : High configuration mode with high cost C 2 : Medium configuration mode with medium cost C 3 : Low configuration mode with low cost
w1 = 0.6 , w2 = 0.2 , and w3 = 0.2 Note that three service modes ( C1 , C 2 , and C 3 )
Figure 6 QA1 calculation with different initial QA1
and their influence factors are specified in the system’s profile, an example used in our simulation is shown in Figure 4.
Similarly, we run the simulation by changing the initial value of QA1 . As shown in Figure 6, we find the similar result that QA1 becomes stable after a few iteration irrespective of initial value.
0
[7] Y. Ye, N. Jain, L. Xia, S. Joshi, I-L. Yen, F. Bastani, K. L. Cureton, M. K. Bowler. A Framework for QoS and Power Management in a Service Cloud Environment with Mobile Devices. 2010 Fifth IEEE International Symposium on Service Oriented System Engineering (SOSE), pp. 236 - 243 [8] Q. Li, Q. Hao, L. Xiao, Z. Li. Adaptive Management of Virtualized Resources in Cloud Computing Using Feedback Control. 2009 1st International Conference on Information Science and Engineering (ICISE), pp. 99 - 102, 2009 [9] Y. Xiao, C. Lin, Y. Jiang, X Chu, X. Shen. Reputation-Based QoS Provisioning in Cloud Computing via Dirichlet Multinomial Model. 2010 IEEE International Conference on Communications (ICC), pp. 1 - 5, 2010 [10] Z. Yan, C. Prehofer. Autonomic Trust Management for a Component Based Software System. IEEE Transactions on Dependable and Secure Computing, 2010. doi. 10.1109/TDSC.2010.47 [11] B. Kosko. Fuzzy Cognitive Maps. International Journal Man-Machine Studies, vol. 24, pp. 65-75, 1986.
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.
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