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Game Theory Based Iaas Services Composition in Cloud Computing


Academic year: 2021

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Game Theory Based Iaas Services Composition in Cloud Computing



Yang Yang,


Zhenqiang Mi,


Jiajia Sun

1, First Author

School of Computer and Communication Engineering, University of Science and

Technology Beijing, yyang@ustb.edu.cn

*2,Corresponding Author

School of Computer and Communication Engineering, University of Science

and Technology Beijing, mizq@ustb.edu.cn


School of Computer and Communication Engineering, University of Science and

Technology Beijing, sunjiajia@ustb.edu.cn


In cloud computing, the service providers often take different strategies in allocating resources in IaaS through composition. This phenomenon will affect the performance of composition process. Moreover, when different consumers apply for the same types of services, the fundamental problem of how to effectively allocate and schedule the services with consideration of various service characteristics rises. Therefore, this paper discusses the gaming behaviors of service composition in cloud computing based on game theory. Through investigating the gaming behaviors of winning cost in different players, A SLA-based service composition algorism is proposed. In the paradigm, the service composition is a process happening among multi service composite participants, through multi rounds of gaming, the participants come to an agreement on a SLA. In this way, each player would get maximal equilibrium benefit while agree on the SLA between both parties of service trading. Several experiments are conducted to evaluate the proposed methods.

Keywords: Cloud Computing, Game Theory, IaaS, Service Composition, SLA

1. Introduction

Cloud Computing has become a focal point in the Computer Science research area through the past years [1] [2] [3]. Cloud Computing is generally said to be the development of Distributed Computing, Parallel Computing and Grid Computing. It uses Virtualization Technology to consolidate multiple physical servers to a resource pool [4] [5], to get a better management and scheduling of these resources, and provides customers with PaaS, SaaS and IaaS services. In these services, IaaS is the base of cloud computing. This kind of model integrates multiple infrastructures like physical resources, virtual resources, data, network etc. Based on IaaS, all kinds of Internet applications can be developed and deployed. Because the management and scheduling of IaaS have a significant impact on the QoS of the Cloud Computing system, this paper mainly consider how to effectively manage and schedule IaaS.

There are many researches on the cloud computing management and scheduling, Amazon EC2 [6], Google App Engine [7], Salesforce [8], Microsoft Azure [9] etc. All these systems can provide customers with the application environments they need and on which to grow up their own businesses. The resource scheduling algorisms used in grid computing and web service composition algorisms can also be used to deal with service composition and scheduling problems we face in cloud computing. Paper [10] studies the service composition and optimization based on AI planning. It defines the cloud computing service composition as the combination of an original interface, objective interface and multi-cloud repository, and proposes a service composition implementation framework in cloud computing. In paper [11], a web service composition model based on message mechanism if given. Message mechanisms of two services are matched in the beginning, and then we process the web services composition. Paper [12] builds a semantics web service composition model based on the ontology domain cost figure. They give us a formal description of web services using ontology techniques and build a directed acyclic graph based on the composition cost of web services. In the end, a depth-first search method is used to find out the optimal composition on the graph. In [13], the author proposes an event-based service composition algorism. It firstly gives a simple service


event language based on ECA rule, and on which to build the composition solutions of composite service using modular method. However, all these algorisms have only considered the services own and ignored the interaction of services dealers’ decisions in the service composition processing. The service participants actually have significant impact on the service composition. Different participants with diverse conditions have various requirements and different objectives. Therefore, the service providers would take different actions and strategies and will surely affect the results of service composition. Meanwhile, when different consumers apply for the same class of services, it would be a key question that how to reasonably allocate and schedule the services considering the requirements of the service requesters and the service features. This paper discusses the gaming behaviors of service composition in cloud computing based on game theory. Study the gaming behaviors of winning cost in different players. A SLA-based service composition algorism is proposed. In the paradigm, the service composition is a process happening among multi service composite participants, through multi rounds of gaming, the participants come to an agreement on a SLA. In this way, each player would get maximal equilibrium benefit while agree on the SLA between both parties of service trading. The service composition and scheduling will be optimized in whole.

2. SLA based IaaS service composition

The cloud computing system integrates physical and virtual resources and provides IaaS, PaaS and SaaS services. In IaaS, infrastructures like CPU and storage are integrated and managed properly. Customers can apply for services on demand like computing, storage, network and so on. Figure 1 shown is the “resource-service-application” three-tier architecture of IaaS.

Figure 1. The three-tier architecture of IaaS

The base tier integrates all kinds of heterogeneous distributed physical resources into a class of VMs by using virtualization technology, converting the original resources to well managing and scheduling computing resource pool, storage pool and network resource pool. The middle tier integrates and schedules resources from the base tier, and packages them into


services providing to the upper tier. The top tier is the applications that can be accessed over the network.

In the process of service composition and trading, QoS plays a vital role in service selection. The QoS properties of service [14] generally include the task execution time, cost, availability, security etc. QoS properties of service are significant factors dealer would consider when making decision in the process of service selection. To guarantee the satisfaction and loyalty of customers on their selecting services, this paper proposes to establish an electronic contract between the service provider and customers. The contract will be used to define the dealing service properties, obligations and responsibilities between service provider and customer. The service level agreement (SLA) is an important in the service contract. In most conditions, a SLA is the agreement of negotiation between service provider and service consumers. It will make sure the service provider get profit while consumers are satisfied. On one hand, for the service provider, this can help them achieve business objectives, which means to pursue benefit increase or to reduce responsibilities caused by unpredictable network interruptions or outages of servers. On the other hand, for service consumers, SLA attempts to guarantee the service performance and maximize customer satisfaction.

Firstly, we define the SLA of service composition in IaaS.

Definition 1: A SLA is defined as a quadruple (N, P, S, R). Then a SLA can be described as table 1:

Table 1. Service level agreement of IaaS

SLA of IaaS Basic Information (N): name, signing date etc.

Contractors (P): information of service provider and consumer Service (S): Service type

Service parameter

Responsibility(R): Service provider Service consumer

The model shows that a SLA consists with for parts:

N is the basic information of SLA, it defines name of SLA, signing time etc.

P={P1, P2}, P1, P2 represent service provider and service consumer. They describe basic information of provider and consumer, for example, dealing budget and requirements, etc.

S={Sn, Sp}, Sn defines service type and Sp defines service parameters. IaaS has three kinds of services: computing service, storage service and network service. When the service type is confirmed, the relevant parameters should be determined. Different types of services need different parameters.

R={R1, R2} are a series of responsibilities. R1, R2 are responsibilities service provider and consumer must abide by in the service dealing. For example, the service consumer must pay fees in the service dealing and the service provider should provide consumer with proper service according to the service rules.

On the basis of analysis mentioned above, we take the service composition process as a multiple dynamic gaming in order to establish an accordant SLA between the service provider and consumer.

3. Service composition scheduling based on the game theory

In the process of service composition, service requestor and service provider have different needs, the service requester wants to carry out their tasks with high quality at a lower price in the short period of time, and service providers hope to sell at high prices to make a profit. This article discusses the limited number of services; the service requester will compete for services by playing bid game, which is that “users sequential bid-Service directly proportional allocation”. In this model, each user bids on the service according to size, budget and other basic condition of their tasks. And the service provider will allocate services proportionally according to the bids that are given by the service requestor. In this process, each service requester must determine their optimal bids based on the possible bids from other requesters. The higher price the service requestor gives the higher proportion of services the service


requestor will get. That is to say, the bids given by service requestor are constrained and affected by other service requestors in the competition.

In the process of this game, to assure the service requestor will receive services with certain QoS, we signed SLA between the two sides of the service transaction. And the rule of transaction and the properties of QoS are regulated in SLA. Through this, we model the selection and combination of service as a gaming process based on SLA, in which the service requestor will reach an agreement.

3.1 related definitions of Games Theory

Game Theory is a theory and method studying conflict or competition [15]. It is a new branch of modern math and also is an important subject of Operations Research [16].

The elements of Game Theory are participants, messages, action, payment etc [17]. The gaming process describes that the participants can decide their own action according to the useful information and this action generate a payment.

Definition 2 (Nash equilibrium): If each player has chosen a strategy and no player can

benefit by changing his or her strategy while the other players keep theirs unchanged, then the current set of strategy choices and the corresponding payoffs constitute Nash equilibrium.

Definition 3 (Nash equilibrium of service composition): In the market of cloud computing, if

there is a state in which each player, service provider and service requestor, has chosen a strategy and no player can benefit by changing his or her strategy while the other players keep theirs unchanged, that is to say each player maximize their own interest, then services achieve its optimal combination and scheduling.

Using method of economic to analyze composition management in cloud computing, following prerequisite must be assumed:

Assumption 1: The participants of cloud computing are selfish. They pursuit to maximize

their own utility;

Assumption 2: The participants of cloud computing are rational. The bargain of services can

proceed only when it is beneficial both on consumers and producers. And they can join or get out of the market freely;

Assumption 3: The participants of cloud computing have complete message about the market

and service;

Assumption 4: In the cloud, there are no differences among same kind of service with same

amount and these services have same price;

3.2 service composition based on gaming and SLA

Under prerequisite stated above, we describe the process of service composition as a SLA process that finally reach an agreement after gaming for lots of times among participants. In this process, the relationship of service requestor and service provider can be expressed as following charts.

Service requestors submit their task request and describe the size of the tasks and the QoS they expect. Service providers make basic definition and description of service provided. During the bargain, service requestors would bid and pay certain amount of fee to buy certain amount of service and service providers would allocate service based on the bids from service requestors. Service provider and service requestor sign SLA before they reach an agreement and conduct their responsibility and obligation as SLA says.

Here, we presume there are N users to bid on the service. User needs M kinds of services to compose to accomplish its task. Every kinds of service can be accomplished by one or more service node. At the same time, we presume the target of users is to minimize the time to finish the tasks under the limit of budget. So, in the process of gaming, how to acquire corresponding services through dynamic biding? To solve the biding policy of users, we make the following parameter settings:


users is , is the sum of prices given by all users except user . The capability of user acquired from m kind of service is . The share of user acquired from m kind of service is


The size of tasks which user accomplished by m kind of service is .

The size of tasks which user accomplished by m kind of service can be represented by the ration of size of tasks and share of services


The actual cost of user to use m kind of service can be measured as = . The budget of user is .

The users’ goal is to minimize the time to finish tasks under the limits of budget, then the question is summarized by :

(3) the question can be solved by Lagrangian method,

(4) is the Lagrangian operator,solve the function:

Bring (3) into (4), then


get the partial derivative of and on function(10), then


According to (5), relation of any two services is , using

to express other service is ,bring this function to the second function of (5) then,

(7) When N=1,there is only one user to bid.



When N>1, there are more than one user to bid on the service at the same time.


After analyzing, we can conclude that the optimal bid on the first task from user is:

Let , is the biding function, , ,

. Meantime, express the capability of paying for the tasks from service requestor , only when , can users bid on the services , , .

As for the services provider, N service requestors’ biding policy on certain service will reach Nash equilibrium. The biding policy is expressed by .

The solution of Nash equilibrium is a set of biding policy, in which the service requestors cannot gain higher utility by changing their own policy only.

To get used to the dynamic of the services in the cloud, we use the finite order consistent game to the valuation of the service to optimize the service composition policy. Using dynamic programming algorithm, that is every service requestor gives its initial bid based on the history bids of the service. When the game among different service requestors begin, users will renew their bids based on the result of their last bid and repeatedly execute the biding policy until it reaches equilibrium.

The Nash equilibrium function of service price and combination of user biding is , the history bid is , the result of first round biding is , then:


Thus, we can obtain , …… .


3.3 CPU resource allocation based on gaming and SLA

Based on the analysis above, we analyze an example of CPU resource. Assuming that a user needs several services to compose to finish a task and CPU service is one of them. When lots of users request CPU resources at the same time, we allocate computing service based on the “users sequential bid - Service directly proportional allocation” model. In the process, we describe the whole biding game and service processing as a gaming for many times to reach SLA. First of all, we define the SLA of CPU computing service. According to the second part of definition to SLA model, we can describe the SLA of CPU service as in table 2:

Table 2. Detailed Service level agreement of IaaS

SLA of IaaS

Basic Information(N):SLA of CPU, Year/month/day Contract Signer(P)

Service provider: provider of CPU service, making profits by selling services

Service consumer: requestor of CPU service, wishing to finish certain amount of computing tasks, having a budget, knowing biding history of same services

Services (S):

Service Type: computing service CPU Service parameter: CPU speed, time , cost

Responsibility (R):

Service provider: bid on the services

Service consumer: allocate services based on the bids

Then, when several CUP resource requestor request at the same time, different requestor will bid on the CPU resources based on the size of tasks, the budget, and biding history. It bids according to the optimal biding policy stated above every time. After biding for many times, it will make sure the bids on one CPU and CPU resources provider will allocate resources direct proportionally.

4. Experiments and analysis

4.1 Experiment setup

Experimentation Hypothesis: We suppose that there are three players take part in the game and the historical pricing of the service is known. To perform a task needs two compositional services, which are CPU service and storage service. Players give their own price according to the historical prices and adjust their price on the basis of the services allocation results one by one. Finally the allocation result will appear.

Parameters Setting: Set two groups of experiment, setup the historical price, service ability, budget, task size. The parameters are shown as in table 3.

Table 3. Experiment parameters

First Group:

Service Ability Task Size Budget Historical Price

User01 (0.9,0.8) (6,4) 120 (5,4)

User02 (0.95,0.8) (3,5) 110 (5,4)

User03 (0.9,0.85) (3,4) 80 (4,4)


Second Group:

Service Ability Task Size Budget Historical Price

User01 (0.9,0.8) (3,4) 100 (5,4)

User02 (0.8,0.9) (2,8) 120 (3,5)

User03 (0.85,0.9) (5,5) 150 (4,7)

(a) Group one (b) Group two

Figure 2. Experiment results

4.2 Experimentation results and analysis

Figure 2 and Figure 3 show that when there are three players take part in the gaming, the service quota vary with the gaming times. From the graph, two groups of experiment with different historical price and budget can reach convergence within limited sequential gaming. That means the service quotas are assigned in equilibrium.

In the sequential gaming strategies, the services and service dealers consist of the whole system. At last, the service allocation is achieved through finite sequential gaming, as is shown in figure 3. The termination condition is meeting when two consecutive load prediction outcomes differ in a very small constant. Service consumers submit their bid functions to game with each other and decide next bid according to last game result. This process repeats until a stable state appears. Each phase gaming will get partial equilibrium and finally get overall balance. In the gaming, each party of dealing takes its own strategy according to different objectives and requirements, and to get the final equilibrium result after finite gaming. Meanwhile, the SLA is met.

5. Conclusion

This paper studies and models IaaS service in cloud computing. We use game theory to solve the complex behavioral relationships when service participants fighting for interest in the dealing process. The service composition process is modeled as a process of multiple gaming to meet the SLA. In the gaming, we propose the service allocation algorism according to the “User sequentially bid-Service direct proportion assign” rule. A simulation of CPU resource allocation in cloud computing environment is given and proved the effectiveness of the algorism in this paper. In the future study, we will extend the single QoS property requirement to multiple QoS properties requirements and discuss the service allocation algorism in multi QoS properties requirements.


6. Acknowledgement

This work was supported by the National Science Foundation of China (Grant No. 61272508, 61202432, 61070182 and Grant No. 61170209), and the China Postdoctoral Science Foundation (Grant No. 2011M500243)

7. References

[1] Michael Armbrust, et.al., “A view of cloud computing”, Communications of the ACM, vol. 53, no. 4, pp. 50-58, 2010.

[2] Bing Li, A Meina Song, Junde Song, "A Distributed QoS-Constraint Task Scheduling Scheme in Cloud Computing Environment: Model and Algorithm", AISS, Vol. 4, No. 5, pp. 283 ~ 291, 2012 [3] Xiao-gang Liu, "The Study of Supply and Marketing Cooperative Information System Based on

Cloud Computing", AISS, Vol. 3, No. 11, pp. 307 ~ 313, 2011

[4] RajkumarBuyya, Chee Shin Yeo and SrikumarVenugopal, “Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities”, 10th IEEE International Conference on High Performance Computing and Communications, pp.5-13, 2008 [5] Sotomayor B, et.al., “Capacity Leasing in Cloud Systems Using the Open-Nebula Engine”, Cloud

Computing and Applications, 2008.

[6] Amazon. Elastic compute cloud [EB/OL],http://aws.amazon.com/ec2. [7] GoogleApp Engine [EB/OL],http: //appengine. google. com

[8] Salesforce. [EB/OL], http://www. salesforce. com

[9] MicrosoftAzure [EB/OL], http: //www.microsoft. com /azure

[10] Guobing Zou1,et.al., “AI Planning and Combinatorial Optimization for Web Service Composition in Cloud Computing”, CCV Conference 2010, pp.17–18, 2011.

[11] Aiqiang Gao, Dongqing Yang and Shiwei Tang, “Web Service Composition Based on Message Schema Analysis”, Lecture Notes in Computer Science, 2007,4443, 918-923.

[12] Wu Chongyun and Wen Jun, “Semantic Web Services Composition Model Based On Domain Ontology Cost Graph”, 2009 Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing, pp. 1-5

[13] Xin li , et.al., “Event-based Web service composition”, Chinese Journal of Software, vol. 20, no. 12, 2009

[14] Jong Myoung Ko , Chang Ouk Kim and Ick-Hyun Kwon, “Quality-of-service oriented web service composition algorithm and planning architecture”, The Journal of Systems and Software, vol. 81, pp. 2079-2090, 2008

[15] Yuanzhuo Wang, Min Yu, Jingyuan Li, Kun Meng, Chuang Lin, Xueqi Cheng, “Stochastic Game Net and Applications in Security Analysis for Enterprise Network”, International Journal of Information Security, vol. 11, no. 1, pp. 41-52, 2012

[16] Gibbons R, Game theory for applied economics, Princeton University Press,1992.

[17] Yuanzhuo Wang, Jingyuan Li, Kun Meng, Chuang Lin, Xueqi Cheng. “Modeling and Security Analysis of Network Using Attack-defence Stochastic Game Net”, Security and Communication Networks. First published online at 17 APR 2012 DOI: 10.1002/sec.535


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