Negotiation Based Service Brokering Using GameTheory

Benay Kumar Ray∗, Sunirmal Khatua† and Sarbani Roy‡

∗School of Mobile Computing and Communication

Jadavpur University, Kolkata, India

Email: roy.binay@gmail.com†Department of Computer Science and Engineering

University of Calcutta, Kolkata, India

Email: skhatuacomp@caluniv.ac.in‡Department of Computer Science and Engineering

Jadavpur University, Kolkata, India

Email: sarbani.roy@cse.jdvu.ac.in

Abstract—Enterprise cloud computing has emerged as apromising technology, where on-demand provisioning of serviceslike storage, infrastructure, software and platform are provided.Growing market of cloud computing, resulted in a variety ofheterogeneous cloud services. This leads to a difficult problem forCloud Service Consumer (SC) when selecting their best fittingCloud Service Providers (SP), who can provide best qualityresource at negotiated price. Thus we propose a middlewarebased Cloud Service Broker (SB) architecture for enterprise cloudcomputing. The objective of SB is to find the most suitable SP fora SC based on negotiation with Service Level Agreement (SLA)parameters like price and quality. Second we propose game theorymodel for automatic SLA negotiation between SC and SP whereCSB provides optimal value of price and quality to both theparties.

I. INTRODUCTION

Cloud computing has emerged as a new computingparadigm that streamlines the on-demand provisioning of In-frastructure as a Service (IaaS), Software as a Service (SaaS),Platform as a Service (PaaS), providing end-users with flexibleand scalable services accessible through the internet [3], [4],[14]. The above mentioned services are generally deliveredthrough data centres with different levels of virtualizationtechnologies. As market of cloud computing grows and numberof infrastructure and service providers increases, the marketcomplexity also increases. Due to market complexity, usershave to deal with many different services, instance types,price schema and cloud interfaces [2]. In this context, cloudbrokering mechanism will be essential to handle a diverserange of services, like selecting proper resources for specificjob type, managing Service Level Agreement (SLA), servicemonitoring for detection of violation in SLA. A cloud brokeris an intermediary organization that simplifies the relationshipbetween a cloud provider and its customer. Cloud brokeraggregate, integrate and customize services for customers, butcloud providers often struggle to provide this type of serviceswithout an intermediary between cloud provider and cloudconsumer.

Cloud users may use different cloud providers, suchas Amazon Ec2, Google Application Engine, GOGrid,CloudSigma for executing different tasks on particular type

of instance ( eg: In Amazon EC2 type of instances are definedbased on configuration, named as small, medium and large).These providers fulfil different cloud user request by providingdifferent types of instances at a particular price. Instances aredifferentiated, based on configuration and each instance type isof different quality as it provides different quality of service. Inthis case difficulty for the SC is to choose the best SP, who canprovide best quality of service at minimal price. Estimation ofresource for executing task at a lower price by SC is not alwaysright and SC may have to pay more. In case of Amazon Ec2,there are twenty three different types of instances and out ofthis, selecting best instances [23] for executing particular taskis not easy. So, for proper estimation of resource, details of tasklike task type (CPU, memory, Disk and network intensive), taskexecution time should be known. Task type can be defined bySC but estimation of task completion time cannot be done. So,it will be not possible to select proper instance for executingtask, so that price for executing task may be minimized. If taskcompletion time is known then it is easy to select resource typeon which task can be scheduled.

Service Level Agreement represents the contractual agree-ment that has been agreed upon Cloud SP and SC that definesthe Quality of Service (QoS), which is achieved through anegotiation process [11], [15]. The whole process of nego-tiation is complicated and challenging as the resources areheterogeneous and a particular resource selection at a lowerprice for executing task depends on the individual requirementand preferences of the SC. SLA negotiation process is essentialbecause each SC are independent entities with varying objec-tives and QoS requirements. However this SLA negotiationprocess should be automated as it cannot be expected that SPand SC have the ability to conduct the negotiation process bythemselves and reach to a mutually acceptable agreement.

So, there must be some scheme by help of which SC maymanage overhead of selecting right SP, so that appropriatequality instance for execution of task may be selected atoptimal price after negotiation. To reduce this overhead servicebroker (SB) are used, which act as intermediaries between SCand SP. In this paper we divide our work in two portions. Firstwe propose our middleware based cloud SB architecture. Theobjective of SB is to find a most suitable SP, who can provide

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best quality instance at negotiated price to SC. SB may saveSC time and extra money and determine task completion time,optimal negotiated price and quality of instance and best SP.Second we provide suitable model for SLA negotiation basedon game theory. In our model SB negotiates SLA on behalfof both SC and SP and provides optimal value for price andquality to both the parties.

The paper is organized as follows. The next sectionpresents related research work. Our service broker resourceprovisioning architecture is presented in Section 3 followedby explanation of different modules. In Section 4 we presentgame theory based SLA negotiation framework followed byexperimental results. Section 5 concludes this paper and out-lines the authors future work.

II. RELATED WORK

A wide range of work in SLAs for cloud computingare carried out during standardizing efforts. The web servicelevel agreements (WSLA) by IBM describe the specificationlanguage for SLA for web services [7]. Cloud computingis dynamic in nature, so QoS parameter has to be monitorcontinuously to enforce SLA. Patel, et al in [8] proposed amechanism for managing SLA in a cloud computing environ-ment based on web service level agreement framework.

Wang, MinChao, et alin [9] proposes a conceptual platformof SLA in cloud computing environment, to evaluate thereliability of providers or resources and propose technique tomake the SLA negotiation between cloud providers and cloudconsumer more convenient and transparent. Lodde, et al in [10]presented SLA-driven resource provisioning in the cloud wherehis aim is to optimize resource utilization while complying tothe negotiated SLA, the minimum amount of resources has tobe determined as exactly as possible. SLA negotiation betweencloud provider and consumer is another part of research areain SLA agreement. Recent research on automated SLA nego-tiation proposes a novel trusted negotiation Broker frameworkthat performs negotiation on behalf of SC on QoS attributewith SP for various services [11], [12], [13]. In the context ofcloud computing, current research provide a limited supportfor automated SLA negotiation in [17] they propose tradeoffapproaches for Cloud service negotiation and in [18] they havepresented multi-issue negotiation mechanism to facilitate priceand time-slot negotiation between cloud agents and tradeoffbetween price and time-slot utilities. There has not been muchwork done in the negotiation between SC and SP based ongame theory in cloud computing where middleware basedservice broker find optimal negotiated value for price andquality. Our work is first step in this direction.

III. ARCHITECTURE

In this section we propose our middleware based SBarchitecture. Firstly, we present the proposed architecture inFig. 1.

In this architecture SB is a third party between SP andSC. SC submits their request for resources to SB. Here SBdeals with SP on behalf of SC and takes the responsibility toselect proper resources for a given task and manage SLA ofselected resources. In our work, we have considered, CloudService Provider Broker (SPB) who handles all the details of

Fig. 1. A middleware based cloud service broker architecture.

cloud IaaS providers and their respective SLA. After selectionof compatible resource for a task, SC and SP can negotiatewith the help of cloud service broker to draft SLA document.

In the following section we will provide the sequence dia-gram of resource brokering in Fig. 2. and explain componentsof proposed SB architecture.

A. Resource Request Requirement (RRR)

It stores details about the SC request. SC request forresource, on which task can be executed at a lesser cost.Consumer input for request are tasks which have to becomputed, task type, that is, whether task is CPU intensive,memory intensive, disk intensive or network intensive and SLAtemplate. These client requests are then forwarded to cloudbroker module Request analyzer.

B. Request Analyzer (RA)

The task submitted by SC is first analysed and checkedin history. If related task details are found in history thenaccordingly proper measurements are taken, otherwise the taskdetails are forwarded to SPB, and SLA template to SLAservices module.

C. History

Our approach maintains a history of all requested taskcompletion time and its corresponding SLA document. Usingthis history data, it may be possible to select proper resourcefor a new requested task by matching similar type of task inhistory. Against each new unmatched task there is an entryin history. The corresponding entries are: estimated execution

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Fig. 2. Sequence diagram for resource brokering showing how different modules of service broker interact with each other.

time of a task (statically analysed time), actual execution timeof task which is measured after originally deploying the taskon selected resource, approximate estimated execution timeof task and drafted SLA documents which are prepared afternegotiation between service provider and consumer.

D. Service Provider Broker (SPB)

It administers a set of cloud service providers. Here SPBact as the owner of a virtual resource, wherein some cloudservice providers are registered as members. SPB recordsdetail of all registered cloud IaaS service providers and theircorresponding SLA details. SPBs work is to provide initialoffered value for computing instance and its correspondingSLA, to SB for specific task type.

E. Execution Time Analyser

On statically analyzing task completion time on specifictype of hardware, the approximate cost of executing thatparticular task on different micro-architecture model can beestimated. Depending on this cost, resources may be selectedand assigned to SC. Therefore, for analyzing task completiontime, we need a time predictable computing platform to enablestatic worst case execution time (ET) analysis. Static worstcase ET analysis is a technique to derive maximum length oftime the task could take to execute on a specific hardwareplatform. ET determines an upper bound execution time of aprogram on specific processor model [16], [19]. In practice,estimation of ET is difficult as most of the methods forfinding a worst case ET involve an abstract interpretation or

approximations. Usually techniques which are used for ETestimation are typically pessimistic, meaning that the estimatedworst case ET is higher than the actual ET. The estimated worstcase ET is guaranteed to be always greater than the actualprogram execution time for any input value.

Input to the ET analyzer may be either source code or bi-nary executable of the program. The core of analyzer operateson programs’ binary executable, because instruction cache andbranch prediction analysis require exact memory addresses ofthe instruction. The analyzer construct the control flow graphof this binary executable and determine the timing estimate ofeach basic block through detailed micro-architectural modeling(abstract processor modeling). The estimated time of basicblock are combined together to estimate ET of the program.

In our research work we have statically predicted executioncycle of task and this value is used to determine execution timeof dedicated task. We have used single core tool chronos andmulti-core tool McAit to determine execution cycle of non-interactive task. We have performed our analysis on Malar-daen benchmark suite [20]. These both tools are based onabstract processor models. Chronos is a single core executiontask analyzer incorporating almost all the timing models ofdifferent modern processors micro-architectural features. Itsmicro-architectural features model in-order and out-of-orderpipelines, dynamic branch prediction and their interactionand instruction caches. The micro-architectural features canbe model depending on available parameter option. Pipelineparameters are superscalarity, instruction fetch queue size andreorder buffer size. Instruction cache parameters are number

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of sets, block size, cache associativity.

McAit predict the timing behavior of multi-core architec-ture [21]. McAit has used local L1 cache and shared bus foreach core. Technique of abstract interpretation is used to ana-lyze cache behavior of particular task running on a dedicatedcore. UPPAAL model checker is used to find execution cycleof respective task.

Amazon EC2 provide instances [23] on basis of computeunits. In Amazon EC2, 1 compute unit is equivalent to 1.1GHz on intel xeon processor. Therefore, a task with c cyclesrequire c× (1/(u×1.1))×10−9s on an Amazon EC2 instancewith u compute units.

F. Service Level Agreement

SLA is an agreement in which all relevant quality at-tributes are specified for the cloud provider and consumer.The specification of existing SLA specified by different cloudproviders for cloud services are not designed for handlingflexibly. Most of the cloud providers put the burden of an SLAviolation notification, monitoring and measurement of SLAmetrics on their customers. Consumers may relieve from theadministrative responsibility, when cloud providers may offeran automated SLA process for SLA monitoring, managementand negotiation. SLA negotiation, management and monitoringin cloud is complex in nature, so cloud brokering mechanismwill be essential in handling a diverse range of SLA services.In this paper we propose SLA brokering mechanism for SLAnegotiation based on game theory. Calculated optimal value ofprice and quality is forwarded to resource selection module,where resource is selected based on offered value of particularinstance for estimated time.

IV. SLA NEGOTIATION

In this paper an automated SLA negotiation mechanism isemployed for cloud computing services. Negotiation is viewedas a bargaining process between SP and SC, by which jointdecision is made by two parties. A SP and SC often have aconflict of interests, so interaction in a single phase may endup with nothing and repeated interaction may be costly andtime consuming. Therefore different preferences and conflictsof interest make it difficult for SP and SC to arrive at acommon agreement. So SB can help them, to reach at anagreement by suggesting optimal value on which both canagree. SB is employed in negotiation specifically because ofminimizing complexity in negotiation for SP and SC. SBs mainaim is to arrive at an agreement that is mutually acceptableand beneficial to both parties involved, and ensure there is noambiguity between them. The negotiation process is complexand may result in a formation of SLA that is not agreed uponby any of the parties. The objective of this work is to estimateoptimal value for SLA on the basis of preference or satisfactionlevel (SL) of SC and SP. The estimation is based on gametheory.

A. Game Theory Overview

Game theory is a branch of mathematical analysis devel-oped to study decision making in strategic situations, whereeach players success in making a decision depends on thedecisions of others. Game theory provides a mathematical

process for selecting an optimum strategy in the face of anopponent who has a strategy of his own. It is often usedin political science, economic, engineering, computer science,evolutionary biology, and philosophy.

The normal form, also known as strategic form is a matrixrepresentation of a simultaneous game of a n-player game ingame theory. It consists of a set of players, a set of strategyprofiles, and a set of payoff functions.

The formal definition of normal form non zero sum gameis given by a triplet (P, A, U) where

• P = {p1, p2, . . . . . . , pn} is a set of players.

• A = S1× S2×, . . . . . . ,Sn is a set of strategy profiles,where Si is a set of actions for the player pi and sidenote the choice of strategy by player i, where si ∈Si, and S={s1, . . . . . .sn} to denote a strategy profile orchoice of strategies by all of the players, where s ∈S.

• U= {u1,u2, . . . . . . ,un} is a set of payoff function,where ui:S→ ℜ is a real valued payoff function forplayer pi(i = 1,2, . . . . . . ,n)

1) Nash Equilibrium: In a n player game if a playerknows how the other player going to play then it wouldbe able to increase his payoff by using some strategy,which is referred as its best response strategy. Let s−i ={s∗1, · · ·s∗i−1,s

∗i ,s

∗i+1, · · · ,s∗n}, be a strategy profile s without

player is strategy. Now, the full strategy profile can be writtenas s = (si,s−i) precise definition of best response strategy isgiven by:Defination 1 A strategy s∗i ∈ Si is a best response strategy to agiven strategy profile s−i for player is, if ui(s∗i ,s−i)≥ ui(si,s−i)for ∀si ∈ Si(i = 1,2, . . . . . . ,n) A set of strategies with theproperty that no player can benefit by changing her strategywhile the other players keep their strategies unchanged, thenthat set of strategies and the corresponding payoffs constitutethe Nash Equilibrium. Based on best response strategy NashEquilibrium can be defines as follows [22].Defination 2 A strategy profile s∗ = (s∗i ,s∗−i) is a Nashequilibrium, if for each player i,s∗i is a best response to s∗−i,ui(s∗i ,s∗−i)≥ ui(si,s∗−i) for ∀si ∈ Si(i = 1,2, . . . . . . ,n).

A tuple of pure strategies {s∗1, · · ·s∗i−1,si,s∗i+1, · · · ,s∗n} is apure equilibrium if, for all is: ui(s∗1, · · ·s∗i−1,si,s∗i+1, · · · ,s∗n) ≥ui(s∗1, · · ·s∗i−1,s

∗i ,s

∗i+1, · · · ,s∗n). Player i cannot find a better

strategy than s∗i if the other player use the remaining strategiesin the equilibrium. Here equilibrium is called as a pure NashEquilibrium.

B. Description of the Game

A SC and SP are considered as a two parties betweenwhom negotiation has to be done. SC always prefers to havegood quality resource at as much as low price whereas SPwill charge price based on the quality of resource he provides.Here for SC price is of paramount importance whereas for SPit is quality which he provides. Both the parties involved innegotiation process through SB. We consider the case whereboth SP and SC submit their SLA template to SB and inturn SB provides them with optimal negotiated value for theirSLA. The negotiation attributes which we have considered are

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TABLE I. STRATEGY PAYOFF MATRIX

�����p1p2 (Po−δ ) (Po) (Po +δ )

(Qo−Δ) 0.46748, 0 .44489 0.45287, 0.46029 0.43826, 0.4757

(Qo) 0.479, 0 .42989 0.46438, 0.44529 0.44977, 0.4607

(Qo +Δ) 0.49051, 0.41489 0.49051, 0.41489 0.46129, 0 .4457

the price P for executing task and the quality Q of selectedinstance. The information in SLA template, submitted by SPand SC individually is given below.SLA template submitted by SC

• Pmaxc is a SCs maximum price value which he can

spend.

• Pminc is a minimum price value which SC may pre-

ferred.

• Qmaxc is a SCs maximum quality value which he may

preferred.

• Qminc is a minimum quality of instance which SC may

preferred.

SLA template submitted by SP

• Pmaxsp is a maximum expected price of SP for providing

particular service.

• Pminsp is a minimum price value of SP for providing

particular service.

• Qmaxsp is a maximum amount of quality which SP can

offer.

• Pminsp is a minimum amount of quality which SP can

offer.

SB negotiates on two attributes price and quality. At everystage of the game, SB tries to minimize the difference inSL at equilibrium point such that both SC and SP may besatisfied.The optimal value is reached at equilibrium pointwhen the difference in SL between SC and SP is near or equalto zero and at this point we get the best price and quality valuewhich satisfies both SC and SP

1) Players: The game can have two players P = (p1, p2)where p1 is represented as SC and p2 is represented as SP.For player p1 price is of paramount importance and for p2 itis quality. These players are trying to find out best strategy toplay with it.

2) Set of Strategies: Set of strategy profile defined byPlayer p1 and p2 are given by A = S1×S2, whereS1 = {(Po−δ ),(Po),(Po +δ )}S2 = {(Qo−Δ),(Qo),(Qo +Δ)}Qo is offered quality.Po is offered price.δ is quality parameter.Δ is price parameter.The strategy payoff matrix for player p1 and p2 can berepresented as in TABLE I.

3) Payoffs: In economics, the payoff function is based onthe level of satisfaction a consumer receives from any basketof goods [16]. In this paper, we have used this to measure the

level of satisfaction that SC receives from SP for offered valueof price and quality attributes and vice versa. In our research,payoff function Uc(P,Q) or Usp(P,Q) are modelled as linearand monotone in two dimensional space. In SLA negotiation,normally different parties will have different payoff functionfor each SLA attribute, depending on the application scenariosand the attributes characteristics. Let us Suppose, price is theSLA attribute to be negotiated. From SCs perspective, thelower the price, the better they can be benefitted and will havehigh payoff value whereas from SPs perspective the higher theprice, the better they are benefited and will have high payoffvalue. We are modelling payoff function for price and qualityfor SC and SP.

a) SC payoff function model:: Thus if offered price ofSP is (Po) then SL of price Sc(P) for SC can be derived fromFig. 3. From, properties of congruent triangles, we can derive

Fig. 3. Diagram showing satisfaction level of price of service consumer.

(Po−Pminc )

(Pmaxc −Pmin

c )=

(Sc(Pmax)−Sc(P))(Sc(Pmax)−Sc(Pmin))

Sc(P) =

⎧⎪⎪⎪⎨

⎪⎪⎪⎩

Sc(Pmax)− |(Po−Pminc )|

|(Pmaxc −Pmin

c )| · (Sc(Pmax)−Sc(Pmin)

Pminc ≤ Po ≤ Pmax

c0 Po > Pmax

c1 Po < Pmin

c

But SL of quality increases as quality is upgraded and ifquality degraded the SL also decreases. Therefore it is givenby:

Sc(Q) =

⎧⎪⎪⎪⎨

⎪⎪⎪⎩

Sc(Qmin)− |(Qo−Qminc )|

|(Qmaxc −Qmin

c )| · (Sc(Qmax)−Sc(Qmin)

Qminc ≤ Qo ≤ Qmax

c1 Qo > Qmax

c0 Qo < Qmin

c

'

b) SP payoff function model:: Similarly for SP payofffunction for price and quality can be derive as:

Ssp(P) =

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

Ssp(Pmin)− |(Po−Pminsp )|

|(Pmaxsp −Pmin

sp )| · (Ssp(Pmax)−Ssp(Pmin)

Pminsp ≤ Po ≤ Pmax

sp1 Po > Pmax

sp0 Po < Pmin

sp

Ssp(Q)=

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

Ssp(Qmax)− |(Qo−Qminsp )|

|(Qmaxsp −Qmin

sp )| · (Ssp(Qmax)−Ssp(Qmin)

Qminsp ≤ Qo ≤ Qmax

sp0 Qo > Qmax

sp1 Qo < Qmin

sp

The minimum SL which we have considered is 0.01.

4) Aggregated total payoff function: As the single SLAattribute payoff function cannot evaluate proposals with mul-tiple SLA attribute. To find the total SL for each SC and SPfor more than one attributes proposals, the payoff functionof all the attributes need to be normalized into a commonpayoff space, which is defined as the total payoff, by applyingweight on each attributes payoff. The total payoff represent thetotal satisfaction of an multi-attribute proposals and the weightw which is considered, represent the factor of importance orpreference of the corresponding attributes payoff U(P,Q) inthe total payoff space. For our work same weight value isconsidered for price and quality. Though the model of thetotal payoff for our research can support negotiation for anynumber of attributes but in our case we have considered onlytwo attribute. Therefore the total payoff for attribute price andquality is given by:

Uc(P,Q) = Sc(P) ·wp +Sc(Q) ·wq

Usp(P,Q) = Ssp(P) ·wp +Ssp(Q) ·wq

These payoff functions help to calculate the payoff value ofeach strategy played by player p1 and p2.We get two differentabsolute payoff values for p1 and p2.

5) Experimental Analysis: SB receives a range of maxi-mum and minimum acceptable price and quality from both SCand SP. Then SB asks SP to offer initially (Po,Qo) value, forestimated time t, between common ranges of preferred valuesof SC and SP. For our experiment we have selected initialoffered value randomly, between common ranges of price andquality of SC and SP. We have assumed, offered value alwaysstart with minimum. For example if Pmax

c = 4808, Pminc = 3114,

Pmaxsp = 5236, Pmin

sp = 3629 the common range is between value4808 and 3629. Based on the offered value, SL of SC and SPare calculated by using payoff function model of SC and SPand calculated SL value is used to generate payoff for thegame. SB tries to find best offered value for SC and SP onincreasing and decreasing the value of quality and price. Gameis repeating each and every round. At every round of the game,pure nash equilibrium value is calculated and its correspondingstrategy is selected as the best strategy for player p1 and p2.This strategy value is selected as new offered value for the nextround of the game. The game continues until at equilibriumpoint, the difference in SL between SC and SP reaches nearto zero. If at any round, offered value Po and Qo is the best

Algorithm 1initialize(){beginPo ←− get priceOffer()Qo ←− get QualityOffer()Ssp(P)←− get SP priceSatisfaction()Ssp(Q)←− get SP qualitySatisfaction()Sc(P)←− get SC priceSatisfaction()Sc(Q)←− get SC qualitySatisfaction()Δ←− get qualityParameter()δ ←− get priceParameter()end}Negotiate(){beginround=1initialize()payoffMatrix ←−buildPayoffMatrix(Sc(P),Sc(Q),Ssp(P),Ssp(Q))current equilibrium←− findNE(payoffMatrix)(Pc,Qc)←−(Po,Qo) //current offer(Pn,Qn)←−updateOffer((Pc,Qc)) //next OfferpayoffMatrix ←−buildPayoffMatrix(Sc(P),Sc(Q),Ssp(P),Ssp(Q))next equilibrium←− findNE(payoffMatrix)current satisfactionDiff←− findSatisfactionDiff(current equilibrium)next satisfactionDiff←− findSatisfactionDiff(next equilibrium)while(| current satisfactionDiff | ≥ | next satisfactionDiff| )(Pc,Qc)←−(Pn,Qn)current equilibrium ←− next equilibrium(Pn,Qn)←−updateOffer((Pc,Qc)) //next OfferpayoffMatrix ←−buildPayoffMatrix(Sc(P),Sc(Q),Ssp(P),Ssp(Q))next equilibrium←− findNE(payoffMatrix)current satisfactionDiff←− findSatisfactionDiff(current equilibrium)next satisfactionDiff←− findSatisfactionDiff(next equilibrium)round ++new satisfactionDiff ←− getSatisfactionDiff()//continue until NE is reached where satisfaction difference is Lowest, i.e,almost equal to zerowendreturn(Po,Qo,round)end}

strategy selected then that value is the optimal offered valueotherwise the strategy for which equilibrium reaches is chosenas the optimal value for SC and SP.

6) Experimental Result: From Fig. 4. we can see howthe optimal values of Po and Qo are estimated for differentpreferred range of price and quality. . In Fig. 4(a). it is seen thatnegotiation starts from offered value of price 4200 and quality77. Here we have considered a constant discount rate Δ = 1and δ = 50. At equilibrium point, SL of SC is 0.49537 and SPis 0.44128. Therefore for the first round, the difference in SLbetween SP and SC is 0.05409. At each round we change theoffered values and finally the difference between SL decreasesto 0.01559 at offered value Po = 4750 and Qo = 88, whichbecomes the optimal value. From Fig. 4(b). we notice givenoffered value is Po = 4184 and Qo = 69 and after ten roundminimum difference in SL is found to be 0.01604 at offeredvalue Po = 4684 and Qo = 79. In Fig. 4(c). we start with theoffered value of Po = 4207 and Qo = 68. The difference in SLat equilibrium point is -0.05381, and after successive incrementin offered value, the optimal value 0.00236 is reached at Po

= 4407 and Qo = 72. In the last Fig. 4(d). it is seen that atselected optimal value, the SL of SC and SP almost equal andtheir minimum difference in SL at equilibrium point for this

(

Fig. 4. Negotiation result showing how optimal value for price and quality is reached and satisfaction level difference between SP and SC is decreased fordifferent offered value.

Optimal value is -0.00008. The example represents a near idealsituation where the difference in SL of SP and SC is the leastwhich show that SP and SC are able to arrive at same SL withrespect to price and quality.

V. CONCLUSION AND FUTURE WORK

Heterogeneity in cloud infrastructure due to growing mar-ket of cloud computing justify the need for a cloud resourcebroker. The resource broker assists service consumers to findthe appropriate service provider, on proper negotiation onSLA parameters like price and quality. In this paper, wepropose a middleware based resource brokering architecturefor cloud eco-system. We also propose a game theory modelfor autonomic service negotiation between the consumer andthe provider.

In this work, our proposed negotiation algorithm finds theoptimal value of the difference between the satisfaction levelsof the provider and the consumer at nash equilibrium. We haveconsidered only price and quality as the SLA parameters. Infuture, we shall try to maximize the satisfaction of consumerand provider considering multiple SLA parameters as part ofthe negotiation process.

ACKNOWLEDGMENT

This research was supported by a Grant “UGC UPE PhaseII. Mobile Computing and Innovative Applications” of UGCand Jadavpur University.

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