an implementation of the multiagent system for market-based cloud resource allocation

8/8/2019 An implementation of the Multiagent System for Market-based Cloud Resource Allocation

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An implementation of the MultiagentSystem for Market-based Cloud

Resource AllocationYee Ming Chen and Hsin-Mei Yeh

Abstract — Multiagent technique offers a promising approach for distributed allocation of Cloud resources without centralized con-trol. The application of market mechanisms for the resource allocation of Cloud computing services is a demanding task, which re-quires bridging economic and associated software agent technical challenges. Dynamic changes in the availability of resources overtime makes the treatment more complicated. Here we employ an allocation mechanism and market mechanism as a market-basedmultiagent resource allocation model to optimal allocate resources through genetic algorithm in a cloud computing environment. Buyerand service provider agents determine their bid and ask prices using k-pricing which sets the transaction price individually for eachmatched buyer-service provider pair. These mechanisms are adaptively to meet the Cloud users/ service providers requirement andconstraints set by bundled services.

Index Terms —Multiagent System Cloud Resource Optimal Allocation

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1 INTRODUCTION

HE last years have seen the emergence of large distri-buted computational systems, such as the CloudComputing, as a new approach to solve large-scale

problems. One of the main challenges that these systemsface is the efficient allocation of Cloud resources. InClouds, resources e.g. processing power, memory, sto-rage, and bandwidth, can be bundled as services, whichare offered to other Cloud users. Cloud service providershave to plan their resource usage carefully and be awareof dynamic changes of the incoming requests for theirservices. Therefore, various resource allocation approach-es are an exciting area of research which have been pro-posed to address the problem[1]. Some of them are basedon analogies between the system and real economies.Commonly called market-based, those approaches offer apromising solution [2,3,4]. In a market, Cloud serviceproviders face dynamic and unpredictable users beha-vior. The way, how prices are set in a dynamic Cloudmarket environment, can influence the demand behaviorof price sensitive users [5.6]. Multiagent system (MAS)incorporates market mechanism into system allowing thesoftware agents to have preferences over some attributesof the allocation, e.g. bundle of Cloud service and its priceof the resources. The market-based multiagent systemalso allow for computational and geographical distribu-tion decentralized implementation while providing me-chanisms to regulate the behaviors of users.

In this paper we consider the multiagent for market-based Cloud resource allocation, which contain allocationmechanism and market-based mechanism. The resourceallocation mechanism needs to satisfy the Cloud serviceproviders and the users. The market-based mechanismthat uses the concept of k-pricing that is iteratively adjusted to find acceptable between a set of demands and alimited supply of Cloud resource allocation. Our goal isto devise the market-based Cloud resource optimal allo-cation that maximizes the utility across all Cloud usersand Cloud service providers(i.e., welfare). Then the fol-lowing economic requirements can be stated:

1) Economic efficiency: When the allocation is eco-nomically efficient, it is impossible to increase a partic-ipant's welfare without decreasing another partici-pant's welfare; i.e. there is no wasted resource. Max-imizing the total welfare is a sufficient condition for

economic efficiency. The proposed allocation mechan-ism employs mixed integer programming to strictlymaximize the total welfare.

2) Double-side rationality: To encourage a fair exchangebetween resource providers (sellers) and users (buy-ers), the prices should only depend on the supplydemand conditions. The proposed market mechan-ism is based on the k-pricing model [7,8] to give noadvantage on the seller's side or the buyer's side.

Therefore, the MAS resource allocation in this marketenvironment could be optimal either for the central entitychoosing the allocation; or with respect to a suitable ag-

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Corresponding author:Yee Ming Chen is with the Department of IndustrialEngineering and Management, Yuan Ze University, Taoyuan, Taiwan.

T



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gregation of the preferences of the individual agents inthe system (e.g. agents may have preferences over thebundles of service of they receive. in addition, they mayalso have preferences over the bundles of services re-ceived by other agents). The rest of the paper is organizedas follows. Section 2 deals with system structure relatedto market-based multiagent Cloud resource allocation. InSection 3, we describe the proposed DPSO based algo-rithm in detail. Experimental results are presented in Sec-tion 4 and some conclusions and future works are pro-vided towards the end.

2 MULTIAGENT FOR MARKET -BASED FOR CLOUDRESOURCE ALLOCATION

The two key roles driving the multiagent for market-based Cloud resource allocation system are: Cloud Ser-vice Providers (CSPs) providing the agents role of sellersor supply and Cloud Users (CUs) representing buyer ordemand. The market-based MAS environments providethe necessary infrastructure including security, informa-tion, transparent access to remote resources, and cloudservices that enable us to bring these two entities togeth-er. Users interact with their own brokers for managingand scheduling their computations on the Cloud. TheCSPs make their Cloud resources enabled by runningsoftware systems along with Cloud Trading Services(CTS) model to enable resource trading and execution ofuser requests directed through CUs. The interaction be-tween CUs and CSPs during resource trading (servicecost establishment) is mediated through a Cloud MarketDirectory (CMD) model (see Figure 1 ). They use variouswelfare model, K-pricing model and interaction protocolsfor deciding Cloud service access price. These models arediscussed in Section 3.

Figure 1 The multiagent for market-based Cloud resource al-location system

CUs can be charged for access to various resources in-cluding CPU cycles, storage, software, and network. TheCSPs users compose their bundles of service using inte-

raction protocols through CMD. The CTS (working forthe CSPs/CUs) can carry out the following steps forCloud resource allocation applications:

1). Identify Cloud service providers.2). Identify suitable Cloud resources and establishes their

prices (by interacting with CSPs and CUs).

3). Select bundles of service that meet its welfare of alloca-tion mechanism (e.g. lower cost and meet deadline re-quirements). It uses genetic algorithm while optimiali-zation Cloud resources and mapping requests to us-ers.

4). Use market mechanism (working with K-pricing) ite-ratively adjusted to find acceptable between a set ofdemands and a limited Cloud supply for trading andissues payments as agreed.

Figure 2 shows an illustration the proposed the market-based multiagent resource allocation model which func-tions embedded in the system as shown in the Figure 1. It

mainly functions consists of two inter-dependent me-chanism: allocation mechanism and market mechanism. There are two sorts of actors in a double side based market:Cloud service provider agents and Cloud user agents (whorepresents the Cloud market institution). As it shows, thesellers and buyers submit sell orders and bid orders (buyorders) to the market institution, respectively. The marketmaker of the market institution sets pairs from these in-coming orders according to the allocation and marketmechanisms that is applied to the market-based environ-ment.

Figure 2 The market-based multiagent resource allocation model

2.1 Allocation mechanism



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We formulate the allocation mechanism into the linearmixed integer program[9,10]

Object function Both buyers/sellers' greatest total wel-fare

Maximize| | | | | | | |

, , , , , , ,1 1 1 1 1 1 1

N G T M N G T

j j k t j k i i k i j k t j k t i j k t w v z q v q y

(1)

Where z denotes whether the resource is allocatedto the Cloud users. y denotes the proportion of quanti-tu q allocated to the buyer accept iv is the minimumprice per timeslot ( T t 1 ) at which the service pro-vider wish to sell the bundled service. Allocation con-straints divided into three types

(I) The type service limiting:

(a) Bundles of service needed by buyer

| |

,10,

G

j k j jk x B u

N j1 (2)(b) Service in the time slot and limit

,0,1 ,,, k jT

t k jt k j xl z(3)

Sk B j 1,1

(II) The proportion of quantity of service limiting

(a) Quantity of proportion of resource paid,1

||

1 ,,, N

j t k ji y(4)

T t Gk M i 1,1,1 (b) The service amount that the buyer receives is equalto the service amount that all sellers offer

,01 ,,,,,,.

M

i t k jik it k jk j yq zq(5)

T t Gk N j 1,1,1

(III) Serve in time slot and limiting

(a) The buyer begins to accept in the period earlierthan buyers and need to accept this service period

,0,,, t k jk j zt a

(6)

(b) Time slot when the buyer accepted and can't belater than buyers and need this service this service periodfinally

,0,,, t k jk j zd t

(7)

T t Gk N j 1,1,1 (c) The seller offers service for the earliest period of all

buyers'

N

j t k jik j yt a1 ,,,, ,0

(8)

T t Gk N j 1,1,1 (d) The seller offers some service for the latest period

of all buyers'

N

j t k jik i yd t 1 ,,,, ,0

(9)T t Gk M i 1,1,1

(e) The buyer acquisite bundles of service or not.

,1,0 ju(10)

N j1 (f) The buyer accepts the service or not

,1,0,k j x(11)

Gk N j 1,1 (g) The buyer accept this service in the time slot or

not

,1,0,, t k j z

(12)1 ,1 ,1 j N k G t T

(h) The seller offers the proportion of service,10 ,,, t k ji y

(13)1 ,1 ,1 ,1i M j N k G t T

One of the main contributions of this work concerns theresolution approach employed. As explained earlier, wehave one objective function and thirteen constraints andsolutions should be searched considering these criteriasimultaneously. To achieve this, we use a genetic algo-rithm(GA) . The evolution of the population takes placefollowing the general GA principles through tournamentselection, crossover and mutation. In addition other ge-netic operators and techniques can be used to improvethe performance of the GA, such as Elitism, It is imple-mented so that the best solution of every generation iscopied to the next so that the possibility of its destructionthrough a genetic operator is eliminated. The flow chartof figure 3 describes the main steps of the GA procedure.

.




Start GA

Iteration finish ?

generation i = i + 1

Elitism

No YesOptimal

Allocation

Feasible solutions check

Initial Solutions

GA operation

For the changed time periods,check the constraints

▪ Random generate a populationof bundles service allocation

▪ Each constraints of binary codesolution with Eq.(2)~(13)

Evaluate fitness functionEq. (1)

▪ Tournament selection▪ Crossover ▪ Mutation

Figure 3 The GA procedure for resource allocation mechanism

2.2 Market mechanism

As introduced above, besides the allocation mechanismin the market-based multiagent resource allocation model,model consists another market mechanism. While theallocation mechanism generally accounts for the technicalspecifics of the Cloud setting, the objective of the pricingcomponent is to induce the selfish participants in themarket-based environment to act towards the generalobjective, in this case welfare maximization. To this end,in this section k-Pricing scheme is presented that can beused in conjunction with the allocation mechanism. k-Pricing is an alternative market mechanism to propor-tional critical value pricing. It was introduced in Schnizleret al. (2008)[11] for double-sided combinatorial auctions.We employ the K-pricing scheme to fix the resource. Let

10k

be an arbitrary proportion. The price P is thencalculated as

))(1()( ,,,,,,,, t k jiit k j jt k ji yvK zvK P (14)

The basic idea is to distribute the welfare generated bythe allocation mechanism between buyers and serviceproviders according to a factor k ]1,0[ . K-pricingenables us to disentangle the risk aspect of discountingfrom the time aspect and allows us to quantify exactlyhow much of an asset’s current price is due to time dis-counting and how much is due to risk discounting.

For instance, assume an allocation of resources from aspecific service provider to a specific buyer. The buyervalues these resources at $10 while the service providerhas a reserve price of $5. Then the (local) welfare of thistransaction is $10- $5 = $5, and k $5 of the surplus isallotted to the buyer (who thus has to pay $10 - k $5)and ( 1- k) $5 is allotted to the service provider (who

thus receives $5+(1-k) $5).k-Pricing has two main advantages. The distributionof welfare among users and providers can be flexibly pre-defined by setting the factor k, thus allowing for bothfairness and revenue considerations[14].

3. MULTIAGENT RESOURCE ALLOCATION SIMULA-TION DEVELOPMENT

We are developing a simulation environment to eva-luate the performance of the proposed market-based mul-tiagent resource allocation model. In this section, we willpresent the experimental results and comparative thecomputational performance. The platform for conductingthe experiments in a PC with Dual Core Processor4400+2.29 GHz CPU and 1.75GB RAM. All programs arecoded in Java programming language in Borland JBuilder2006. The simulation environment consists of buy-er/service provider agents and a market exchange. Theindividual agent has its own preferences to decide the bidaccording to its budget and the market trend. The pricesand payments generated by proportional critical valuepricing and k-Pricing with k = 0.5. After we know the

basic information, the buyer agents and the service pro-vider agents start to trade with each other which are illu-strated as Figure 3.

Figure 3 Trade exchange for buyer agents and the Cloud serviceprovider agents

From figure 4 to 7 illustrate a series of trading among




seven participants: three Cloud service provider agentsoffer three bundled services. Buyer agent 1, 2, 3 and 4get their need different bundled service simultaneously.

Figure 4 Buyer agent 1 with all service provider agents re-source allocation after trading

Figure 5 Buyer agent 2 with all service provider agents re-source allocation after tradin g



As buyer's number increases, the degree of competitionamong the buyers also increases. When the competition isthe stronger , will also be high buyer's bid. They wouldlike to bid by higher than the lowest bid price offered atthat timing, we call that jump bidding in this behavior.That is the ratio of jump bidding( ROJB):

iceCleaningiceCleaningg JumpBiddin

ROJBPr

Pr (15)




The higher in k value, the cleaning price Inclined to buy-er's bid price. This shows that it is comparatively favora-ble to the seller. therefore both buyer/sellers' greatestwelfare will increase as shown in Table 1.

Table 1 Different ROJB and k value are produced welfare ( w)

K valueROJB

1 1.1 1.2 1.3 1.4

0.3 61.52 73.35 116.01 143.97 144.33

0.5 65.21 82.79 120.21 135.16 146.75

0.7 67.52 97.35 128.39 148.87 203.08

Different k value and ROJB value were produced wel-fare as the figure 8 . The buyer would like to offer it withthe higher price, then welfare( w) rising also becomesmore and more obvious. Safeguard one's own interestsand incline to choose higher k value. Not only can makewelfare value improve like this , but also make the tradesucceed.

Figure 8 Different welfare (w) in various k and ROJB

Therefore, if can accept higher k value with the attitude ofcompromise, both buyers/sellers' greatest total welfarewill also increase.

4. CONCLUSIONS AND FUTURE WORK

In this paper we proposed the market-based multia-gent resource allocation model on cloud computing envi-ronment. It allows buyers to order an arbitrary composi-tion of services to different service providers. The pro-posed allocation mechanism and market mechanism de-velop MAS simulation environment to evaluate the per-formance of the proposed model. The preliminary simula-

tions show that the model works properly. We are inter-ested in the behavior of the trading exchange, particularlythe interaction between Cloud user agents and Cloudservice provider agents. Market –based model modelshows promise for enhancing resource allocation andpricing. We are now working toward GA and K-pricingschemes find an interesting match between scalability

and individual behavior. By means of MAS simulations,the effect of multiple Cloud users and providers strategicbehavior might be investigated. Extensions of the heuris-tic allocation scheme, such as theuse of more sophisticated norms in the sorting phase, aswell as the study of their impact on the mechanisms ’ strategic properties, might be further promising areas forfuture research.

ACKNOWLEDGEMENTS

This research work was sponsored by the NationalScience Council, R.O.C., under project number NSC99-2221-E-155-022.

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Biography of the authors:

Yee Ming Chen is a professor in the Departmentof Industrial Engineering and Management at Yu-an Ze University, where he carries out basic andapplied research in agent-based computing. Hiscurrent research interests include soft computing,

supply chain management, and pattern recogni-tion.

Hsin-Mei Yeh was a graduated student in the De-partment of Industrial Engineering and Manage-ment at Yuan Ze University, where she was study-ing basic and applied research in Cloud computingand heuristic methods. She now works in AU Op-tronics Corporation as Engineering.

an implementation of the multiagent system for market-based cloud resource allocation

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