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Future Generation Computer Systems 27 (2011) 10561069

Contents lists available at ScienceDirect

Future Generation Computer Systems

journal homepage: www.elsevier.com/locate/fgcs

A survey of economic models in grid computing

Aminul Haque a,, Saadat M. Alhashmi a, Rajendran Parthiban b

a School of Information Technology, Monash University Sunway Campus, Jalan Lagoon Selatan, Bandar Sunway, 46150, Selangor Darul Ehsan, Malaysiab School of Engineering, Monash University Sunway Campus, Jalan Lagoon Selatan, Bandar Sunway, 46150, Selangor Darul Ehsan, Malaysia

a r t i c l e i n f o

Article history:

Received 25 June 2010Received in revised form7 April 2011

Accepted 11 April 2011

Available online 16 April 2011

Keywords:

Grid computing

Economic model

Dynamic environment

a b s t r a c t

Grid computing offers the network of large scale computing resources. Economic models are effective in

collaborating large scaleheterogeneous gridresources thatare typically owned by differentorganizations.Not all the models provide same benefits for users in utilizing the resources. Similarly, the profit earnedby resource providers also differs fordifferent economic models. We surveythe economic modelsused ingrid computing since its inception until 2010. We discuss theiradvantages and disadvantages and analyzetheir suitability for usage in a dynamic grid environment. To the best of our knowledge, no such surveyhas been conducted in the literature up to now.

2011 Elsevier B.V. All rights reserved.

1. Introduction

Investigation of some problems in science, engineering and

commerce such as protein analysis, material properties, and eco-nomic forecasting are computationally complex. By realizing theinsufficiency of a single computer, a cluster or even a supercom-puter in solving these problems, grid computing was initiated inthe mid 1990s [1]. The technology that aggregates distributedcomputer resources across the world is called grid computing. Co-ordination of distributed and heterogeneous computing resourcescreates virtual organizations that support the utilization of idleresources [2]. However, seamless collaboration is a challenge dueto the extreme heterogeneity of these resources. This hetero-geneity is due to varying architectures architecture of physicalresources (e.g. clusters, supercomputers, ordinary PCs), differentadministrative domains (e.g. country, enterprize) and multiple op-erating systems (e.g. UNIX variants, Windows). There is also a lackof a uniform way to use these resources.

Fig. 1 shows layers and different components that constitutea typical grid. The layered grid architecture usually rests onthe fabric layer that consists of servers, clusters, monitors andall other distributed computing resources around the world.Mercury [3] system is a good example for this layer. The layerthat controls and allows secure access to the components offabric layer is called the core middleware layer. It also supports

Corresponding author. Tel.: +603 5516 1907; fax: +603 5514 6129.

E-mail addresses: [email protected](A. Haque),

[email protected](S.M. Alhashmi),

[email protected](R. Parthiban).

Fig. 1. Layered grid architecture with examples.

trading and information updating of resources. Globus [4] is awell-known middleware service, which allows resource discovery,management and security. On the other hand, Gridbus [5]middleware supports business driven technologies aimed atutility based computing. Gridbus uses economic models that aidefficient management of shared resources through maintainingthe supply and demand of distributed resources. In this paper,we focus on suitable economic models in grid computing andtheir practicality of usage in different perspectives. The upperlevel of core grid middleware is called user level middlewarethat provides API (Application Programming Interface), libraries,application development environments and resource mediator,

0167-739X/$ see front matter 2011 Elsevier B.V. All rights reserved.doi:10.1016/j.future.2011.04.009
http://dx.doi.org/10.1016/j.future.2011.04.009http://www.elsevier.com/locate/fgcshttp://www.elsevier.com/locate/fgcsmailto:[email protected]:[email protected]:[email protected]://dx.doi.org/10.1016/j.future.2011.04.009http://dx.doi.org/10.1016/j.future.2011.04.009mailto:[email protected]:[email protected]:[email protected]://www.elsevier.com/locate/fgcshttp://www.elsevier.com/locate/fgcshttp://dx.doi.org/10.1016/j.future.2011.04.009


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A. Haque et al. / Future Generation Computer Systems 27 (2011) 10561069 1057

Fig. 2. A reference Market oriented overview of a layered Grid architecture [2].

which negotiates between users and providers, and schedulesapplication tasks for execution on global resources. The SimpleAPI Grid Applications (SAGA) [6] and Triana [7] are the twoexamples of user level middleware. This middleware is used tocommunicate with the core middleware. Grid applications, thefourth layer, is typically developed using the components ofuser level middleware. This layer supports users to execute theirapplications on remote resources and collect results from them

using web portals or applications such as the Grid ApplicationToolkit (GAT) [8] and java Commodity Grid kit (CoG) [9].

The core part of the grid is called core grid middleware,since it offers all the necessary functions such as scheduling,security, data transfer, trading and communication [10]. The mainobjective of this middleware is to hide the heterogeneous natureand provide a homogeneous and flexible environment to endusers. Trading is one of the main parts that motivates resourceproviders to contribute their resources on grid computing. Inaddition, price is a key deciding factor in resource use [11].Price can further be used to maintain equilibrium betweensupply and demand, distinguish different QoS (Quality of Service)requirements and utilize idle resources. A market orientedmodeling can be used in solving distributed resource management

problems such as site autonomy problem, objective optimizationproblem and cost management problem [12]. The site autonomyproblem could occur while accessing resources that belong todifferent administrative domains. The objective optimizationproblem occurs when users want to optimize their QoS andwhen providers want to maximize their profit. Grid resourceproviders need to support seamless management of differentrequests from different users simultaneouslythis known as thecost managementproblem. Several economic models are proposedin the literature for driving market-oriented grid computing. Oneeconomic model is different from the others in pricing resourcesfor varying scenarios.

In this article, we investigate suitable economic models forgrid computing. The analysis covers models that have been

proposed since the inception of grid computing and discusses theirstrengths and weaknesses as perceived by researchers. Finally,

we identify that different models are suitable for dealing withdifferent grid scenarios; however we find no work consideringmultiple economic models and switching between them forvarying scenarios in a grid environment.

The remainder of this article is organized as follows: InSection 2, we explore market oriented grid computing. Section 3explains the strengths and weaknesses of different economicmodels that have been proposed and used by different grid

computing researchers thus far. Section 4 maps out some futureresearch directions in market oriented grid based on the analysesof the models.

2. Market oriented grid computing

Standardization, usability and business models have been ac-cepted as the main success factors for next generation computingsystems [13]. However, market based computing mechanisms aredifferent from the traditional mechanisms in terms of value (i.e.QoS) delivered to a user. The value could be measured by the fol-lowing:

flexibility in parameterization of user driven jobs, suitability of business models for different user requirements

and strategies and adaptation to changes in resource availability, capability and

pricing.

To realize this, market oriented computing organizations needto be more complex than the traditional systems.

Fig. 2 presents the four grid layers of Fig. 1 in terms of marketoriented modeling environment. Each layer has some additionalfunctional entities along with dependencies among them. Thearrow from A to B refers to the dependency of A on B. In amarket oriented architecture, market directory service keeps theresource information updated and helps to generate a competitivemarket price for a particular type of resource. Market basedmiddleware supports market participants to trade grid resources.

Itperformstradingactivities such as SLA(ServiceLevel Agreement)enforcement and billing, contract and trading management. All


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1058 A. Haque et al. / Future Generation Computer Systems 27 (2011) 10561069

of these interact with resources to decide which resources areallocated to which user, for what price and for how long.Economically Enhanced Resource Management (EERM) isolatesusers from its providers based on certain market relevant featuresto increase its functionality. EERM also keeps itself updated withthe resource state through monitoring services and reports to theSLA enforcement. EERM gets information from supply modelingand assists to form SLA. Supply modeling depends on demandmodeling, which provides necessary tools to specify resourceproperties. Offersare generatedbased on both supplyand businessmodels. On the other hand, bids are generated based on usersdemands and preferences (e.g. economic preferences). A resourcemediator negotiates between resource users and providers. Aresource mediator could be a resource broker or a resource agent.Market oriented grids require adaptive management capabilitiesamong different functional entities to enhance the service qualitydelivered to users as well as to optimize providers goals.

2.1. Inspiration for economic grids

Technology, business and policy are interdependent; withouttechnology there are no services and products to be invented,without business models no policies are needed to regulatetheir actions [14]. Buyya argues that the grids heterogeneityand decentralization is similar to the present standard humaneconomy [15], where market based mechanisms could be used tosuccessfully manage the environment. He further argues that thisapproach would be efficient for balancing supply and demand andit is scalable (no need for central coordinator during negotiation).Additionally, it improves the utilization of idle resources anddistinguishes different quality of services. Similar measurementsof a market based grid can be seen in [1620].

Traditional market pricing models for managing grid resourceswould also be applicable for managing self-interested and self-regulating entities (resource owners and users) [21]. A studyundertaken by Chris and Giorgos [11] demonstrates the possible

macroeconomic value for the introduction of grid computing andforecasts a huge amount of gain through the deployment of highperformance grid computing and web service applications. Thepaper argues that the price impact could be very important forindustrial firms which use grid computing. Using grid computing,the firms become more competitive than might otherwise beexpected. Grid technology enables the compilation of resourcesacross many budget boundaries (accessing different economicgoals). Therefore, an appropriate business model would be thekey term for fair dynamic resource collaboration. Price can be akey deciding factor in resource use as well. In a market orientedapproach, uncertainty drives a large portion on the decisions suchas what is available when and for what price [11]. According toone of the leading grid computing resource institutes, The 451

Group, the application of resource trading and allocation modelsis one of the crucial success factors for establishing commercialgrids [22]. Therefore, a suitable pricing model for Internet servicesis one of the main prerequisites for successfully running theimplementation of an accounting and charging system. Shin Yeoand Buyya [23] focus on a pricing mechanism to support utilitydriven management and allocation of resources. Accordingly, theproviders should have mechanisms for generic pricing schemesto increase system utilization and protocols that help them offercompetitive services.

Due to the significance of pricing in grid computing, a robustand viable economic model is required to deal with pricingdistributed resources across multiple administrative domains.In addition, economic models help providers to treat different

users differently based on their requirements and organizecorresponding SLAs, which collectively would construct a rigid

market oriented computing environment. Buyya proposes severaleconomic models (such as commodity market, posted price, andbartering) including both micro and macroeconomic principles fordistributed resource management[15]. However, he only discussesa hypothetical suitability of these models for a grid environment.Not all the models proposed are suitable to deal with all differentscenarios as we will see in the following sections. In Section 3,we discuss about the models that have been studied and analyzedby different grid researchers. We address the strengths andweaknesses of these models in terms of managing heterogeneousgrid resources.Before starting with various economic models in thegrid, we first introduce various criteria that can be used as probesto judge economic grids.

2.2. Criteria to judge economic grids

Economic models are different from one another in terms of theway(i) they areused forinteractionamongusers andproviders, (ii)they are used for pricing, and (iii) they adapt to evaluate differentrequirements. In a grid computing environment, the strengths andweaknesses of an economic model can be evaluated using several

criteria.Some of them arementioned here with a brief explanation.Admission control: Admission control refers to the control ofsubmitting new jobs in a grid to execute. This feature plays animportant role in maintaining market equilibrium. In a marketoriented grid, dynamic pricing can be used to maintain equilibriumbetween supply and demand that change dynamically.

Broadcasting overhead: This is also known as distribution andcommunication overhead. It is the delay incurred to disseminateinformation regarding resource availability, pricing bids etc. overthe Internet. It also depends on geographic distance of computingendpoints, Internet speed and communication protocols.

Computation efficiency: This is the amount of computation timethat is consumed by a particular model while evaluating usersrequests. Models that consume fewer computation cycles areconsidered to be computationally efficient.

Decentralization: Decentralization in an economic based grid refersto the freedom of setting a resource price by a provider. In adistributed environment such as grid computing, decentralizationin pricing is expected to achieve large scale resource collaboration.It can also be used to evaluate global allocation efficiency.

Evaluating market price: The market price or economic price ofa particular resource could be manipulated by the price offeredfor the same resource by different providers. The demand on aparticular resource also contributes to determine the market priceof that resource. A true market price is crucial in achieving acompetitive grid market.

Handling large number of users: This criterion refers to the ability

to contact and evaluate a large number of requests with theirdifferent QoS requirements within a particular period of time.Typically the grid resources are utilized by global users overthe Internet. Hence, there could be many users in general. Thiscriterion could also refer to the scalability of dealing with manyusers.

Job cancelation rate: This is the rate at which requested orsubmittedjobs are canceled by a particular market. Jobcancelationcould occur due to the disagreement of prices, unavailability ofresources and failure while executing the jobs in the grid.

Price stability: This criterion explains the stability of a marketprice for a particular resource for a specific amount of time.Inflation is the opposite of price stability. Inflation is the raising of

resource prices. Again price stabilityis crucial to ensure schedulingstability [24].


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Pareto optimal allocation: This is a resource allocation process, inwhich allocating of a particular resource is not supposed to affectother resources that are currently being allocated or executed [25].This is necessary in grid computing to get the jobs of currentusers done by their requested deadlines. Economic models play animportant role here in setting different SLAs for different users.

Utility based negotiation: This is also called individual rationality,

which refers to the payoff gained through participating innegotiation. In an agent based negotiation, individual rationalitymeans, all the agents in the system agree to participate innegotiation, since each of them is individually assured of receivinga better payoff than in the case of not participating [26]. In a gridmarket, negotiation could happen between a user and a providerindividually in order to optimize their goals or objectives.

Resource allocation efficiency: The ability to allocate an appropriateamount of resources according to the needs of users is calledresource allocation efficiency [27]. It would help users to gettheir jobs executed within their job deadlines. Similarly, providerswould be benefited through provisioning their resources to users.

Economic efficiency: The economic efficiency of a particulareconomic model defines how efficient the model is in utilizing idle

resources as well as maximizing profit for providers. Profit for aparticular period could be presented as a subtraction between thetotal revenue gained and total expenses associated with providingservices throughout that period. In fact, economic efficiencydepends on all the criteria mentioned above. In addition, in ausers point of view, a model can be efficient if it supports theusers requirements. Therefore, the economic efficiency could alsobe treated as user provider efficiency. Social welfare can be usedto determine economic efficiency. Social welfare is calculated byaggregating users and providers utility, which for a particularentity (e.g. user/provider) is defined as the difference betweenhis/her reservation-price1 and agreed-price2 for a particularservice.

3. Economic modelsand their strengthsand weaknessesin gridcomputing

In this section, we investigate different economic modelsproposed by different grid computing researchers since theinitiation of the grid. We briefly explain about different economicmodels, since an extensive explanation on different models hasalready been given by Buyya et al. [15]. The main focus of thissection is to present the economic models in terms of theirstrengths and weaknesses as identified by different grid researchesat different times. At first, we present the strengths of differentmodels in Table 1.

Table 1 demonstrates the significance of economic models ingrid computing. The papers are presented in descending ordersof years so that the reader can easily perceive recent works oneconomic models. It canbe seen from thetable that theCommoditymarket and DA are the most widely proposed models in thegrid. The Commodity market model has the ability to maintainmarket equilibrium, which is crucial for any market-oriented gridenvironment. Maintaining supply and demand by regulating pricebehavior ensures the higher probability to deliver requested QoSas well as increased system performance. The main purpose ofthis model is to determine a supply and demand equilibrium/spotprice. For example, if demand for a resource exceeds its supplyat a particular state, the price of that resource increases in away such that the demand function shifts to the point closer to

1Price limit at which a user/provider has agreed to buy/sell a particular service.2 Price at which both user and provider are satisfied to trade.

the available supply. Various techniques are used to determinethe equilibrium/spot price in the literature [33]. DA, on theother hand is suitable due to its decentralized nature and theability to handle large number of users. In grid computing, usersand providers are self-interested entities and appear with theirindividual optimization strategies. Hence, DA supports them bysorting their valuations and thus expediting the trading phasewithout any requirement of global information.

English auction is another interesting model in the grid,where auctioneer seeks to obtain the true market value of theresource that has been set for auction. Usually, users are free toincrease their bids exceeding others for the resource that theyare competing for. When no bidder is willing to increase theirbids any more, the auction ends and the auctioneer checks itsreservation price with the last highest bid and determines thewinner. This model is found to be suitable for increasing revenue,since it supports competition among users and finally selects theuser who bids the highest by using an iterative bidding policy. Thisalso helps to identify the demand of a particular resource in themarket. However, English auction, in a distributed environmentmay produce network congestion due to its high communicationdemand. English auction, by nature, is an iterativemodeland hence

causes too many messages to be exchanged during the auctionprocess [55].

By using the Bargaining model in grid computing, users andproviders can optimize their preferred interests. The model allowsits participants to negotiate their preferences and finally constructa satisfactory SLA. In grid computing, the preferences could beover budget/job-execution-cost, deadline/job-execution-time orany such criteria. However, successful negotiation also depends onpreference values. For example, if a user and a provider negotiatethe same preference (e.g. deadline & job-execution-time) value,either the negotiation will finish with minimum optimizationof the preference or it will fail. The optimization of a specificpreference fora particular user canbe measured byusingthe utilityfunction of the optimization criteria for that user. Researchers

have already analyzed how to relax different negotiation termsduring the bargaining process, so that better optimization canbe achieved [75,78]. The Bargaining model also requires highcommunication demand due to the multi-round communicationprocess which may not be suitable when there is a large number ofusers.

A proportional share-based allocation is efficient for gridcomputing, since it allows sharing of resources according usersdemands. The model also helps to construct a large scale sharedinfrastructure which is one of the main goals of grid computing.Because of sharing the same resources by multiple users,utilization for the resources increases and hence job cancelationrate decreases. However, failure to provide a sustainable sharingmechanism may cause a lower QoS received by users or even

cancelation of jobs.CNP allows users to choose appropriate service providers

based on their varied requirements. The model permits users tooptimize their preferences (e.g. budget, deadline) by selecting oneor more appropriate providersout of multipleproviders in thegrid.Providers are allowed to cooperate among themselves in order toensure that the service for users is as per the contract. This modelbasically focuses on the users side to optimize their preferencesthan for the providers. Hence, the utility for users is generallygreater than that for providers.

If we observe Table 1 as a whole, we can see that differentmodels are suitable for different scenarios. For example, theCommodity market model is suitable for market equilibrium,admission control and Pareto optimal allocation, whereas the

DA model shows strengths in handling a large number of users,decentralization and time efficiency. The English auction model


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Table

1

Strengthsofeconomicmodelsingridcomputing.

Economicmodel

Strengths

Proposedby:resea

rchfocus/contribution

Commo

ditymarket:Ingeneral,resourcesare

pricedinsuchawaysothat

equilibriumbetweensupplyanddemandismaintained.Therearetwo

typesofCommoditymarketmodelingeneral:flatpricingmodeland

supplyanddemandbasedpricingmodel.Th

elatterismorepopular

amongresearchers,sinceithasthecapabilitytomaintainequilibrium

betweenresourcesupplyanddemandbychangingpricebehavior

Admissioncontrol,computingefficiency,

economicefficiency,flexibilityin

evaluatingmarketprice,paretooptimal

allocation,resou

rceallocationefficiency,

pricestability

Bossenbroeketal.

[28]:Minimizeriskassociatedwithserviceoffering/requestingdue

topricevolatilityb

yadoptinghedgestrategya

Saurabhetal.[29]:Minimizingcostandexecutiontimeusingmeta

scheduling

heuristics

Nimrod/G[30]:Economicframeworkforservicedrivenresourcescheduling

Kevin[31]:Compa

reeconomicbasedapproachovernoneconomic

centralized

approaches

Chengetal.[32]:U

tilizegridresourcesbasedonappropriateserviceselection

Shinetal.[23]:Supportusercentricjobspecificationforsuitableallocationdecisions

Stueretal.[33]:Achieveeffectiveallocationwhileensuringpricest

abilityandservice

fairness

Omer[34]:Econom

icbasedschedulingfortimecostoptimizationa

ndparameter

sweepapplications

DIRSSG[35]:Minimizingjobcancelationrate,whileensuringscalabilityandload

balance

Chunlin[36]:Optimizeaggregateutilizationforgridusers,whilemaximizingrevenue

forproviders

Chunlinetal.[37]:

Studyutilitybasedallocationalgorithmpropertiesunderbudget

andtimeconstrain

ts

Nimrod/G,Gridbus[38]:Schedulecomputationallycomplexandda

taintensive

applicationsunder

economicdrivengrid

Tianfield[39]:Agentbasednegotiationfordistributedresourcemanagement

Gridmarket[40]:E

ffectivetaskschedulinginsupplyanddemanddrivengrid

computing

Gcommerce[24]:

Comparecommodityandauctionprotocolseffectivenessinterms

ofmarketcontrol

GRACE[41]:Dynamicresourcetradingforflexibleapplicationscheduling

Nimrod/G[42]:Schedulingusingadaptivemanagementofcomputationalresources

Kurt[43]:AcomparativestudybetweencommoditymarketandVickreyauction

modelsincaseofp

ricestability,fairnessofallocationsandcommun

ication

requirements

Dou

bleAuct

ion

(DA):Providersarearranged

inascendingorderandusers

indescendingorderintermsofdemandand

budgetrespectively.Ifa

usersrequestmatcheswithaprovidersoffer,thetradeisperformed.

TherearetwotypesofDA;ContinuousDoub

leAuction(CDA)(inwhich

usersposttheirrequirementsandbudgets,andserviceproviderspost

theiroffersatanytimeduringthetradingperiod)andperiodicDA

(whereauctioncontinuesforaspecifictime

periodasdefinedbythe

auctioneer).Theformertypeisdiscussedmostlyinthegridliterature

Marketcompetition,resourceallocation

efficiency,broadcastingoverhead,

computingeffic

iency,handlinglarge

numberofusers,pricestability,

decentralization

,economicefficiency

Izakianetal.[44]:Maximizetaskcompletionrate,utilizationofresourcesandprofit

forproviders

Lietal.[45]:Supportcombinatorialbidsandexhibitincentivecharacteristicsforboth

usersandprovider

s

Wang[46]:Motiva

teusersandprovidersthroughsupportingindividualrationality

Lynaretal.[47]:Studyvariationoftimeandenergyconsumptionbyapplying

differentauctionp

rotocols

Suri[48]:Maintain

pricestabilityusingknowledgebasedpolicy

Valkenhoefetal.[49]:CompareTCDAbwithtraditionalCDAinterm

sofexecution

uncertainty

Wieczoreketal.[5

0]:Studyworkflowbehaviortosupportfasteran

dcheaper

execution

Streitbergeretal.[

51]:Comparecentralizedanddecentralizedserviceallocationin

termsoftimeandutilityfunction


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Tab

le1(continue

d)

Economicmodel

Strengths

Proposedby:resea


Pourebrahimietal.[52]:Decisionmakingagentsadapttodynamicnetwork

environmentandp

ricing

Tan[53]:Investiga

temarketpricethroughprovidingabiddingadju

stmentstrategy

Wengetal.[54]:R

esourcetypebasedmodelingtosupportdynamicadjustmentof

pricing

Marcos[55]:Comp

arecommunicationoverheadandprofitfordiffe

rentauction

models

Pourebrahimietal.[56]:Studypricingfunctioninbalancedandunbalancednetworks

withselfinterestedagents

SX[57]:Supportorganizationstofederatestorageservicesamongt

hemandlease

themglobally

Eymannetal.[58]:DesignALNcforbothcentralizedanddecentralizedorganizations

Kant[59]:AcomparativeapproachofdifferentDAprotocolstomax

imizeresource

utilization

Nimrod/G,Gridbus[38]:Schedulecomputationallycomplexandda

taintensive

applicationsunder

economicdrivengrid

BrickWorld[25]:M

ultiplesingleitemauctiontoavoidcomplexityw

ithcombinatorial

bids

Gridmarket[40]:S

tudypricingalgorithmsforusersandproviderss

eparatelyto

measuretheintegrityofrequestsandoffers

Grosu[60]:Studyeconomicefficiencyandsystemperformanceofthreeauction

models

Gomoluch[61]:Studysystemload,heterogeneityandcommunicationdelaywith

threedifferentmodels

Heetal.[62]:Manipulateandadapttodynamicmarketpriceusing

fuzzylogic

Eng

lishauction:Accordingtothistypeofauc

tion,usersarefreeto

increasetheirbidsovertakingothers.When

nonewouldliketoincrease

thepriceanymore,theauctionends.Theau

ctioneerdeclaresthehighest

bidderaswinner.Bidscanbeproposedfora

singleitem(singleattribute)

orformultipleitems(multiattributes)

QoS,economicefficiency,revenue,

resourceallocat

ionefficiency

Xing[63]:Develop

resourcemappingalgorithmsusingiterativecom

binatorial

auctionmechanism

MACE[64]:Design

anauctionbasedconstructiveeconomicmodeltoassistgridusers

inexpressingtheir

truedemands

Becketal.[17]:Studyeconomicefficiencywhileprovidingsuitable

allocationand

learningmodels

GEMSS[65]:Imple

mentasimulationtoolkitconsideringtherisingneedsfrom

medicalservices

Attanasioetal.[66

]:Developauctionmechanisms,whileensuringminimal

communicationov

erheadwithefficientresourceusage

EGG[67]:Simplify

jobschedulingthroughandeconomicplatformindecentralized

grids

BrickWorld[25]:A

uctionwithmanyitemstoavoidcomplexityincombinatorialbids

Tianfield[39]:Studyagenttechnologyforadaptive,runtimeefficiencyand

autonomousgrid

Bellagio[68]:Analyzescalability,efficiencyandlongtermbehavior

forresources

allocatedinfederatedgrid

Barga

ining:Inthismodel,usersliketogetlo

weraccesspriceandhigher

usageduration.Theprovidersliketogetmoreprofitthroughbargaining.

Theusersmightstartwithaverylowpricea

ndproviderswithahigher

price.Bargainingmaycontinueovermultipleattributes(e.g.price,

deadline/job-execution-time)

Utilitybasednegotiation

Subrataetal.[69]:

Developsemi-staticschedulingalgorithmtomaximizeutilityfor

providers

(continue

donnextpage)


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Tab

le1(continue

d)

Economicmodel

Strengths

Proposedby:resea


Zhao[70]:Maintainmarketequilibriumandmaximizeprofitthroughself-adaptive

autonomousnegotiation

Quan[71]:Determ

inemarketpricebasedondeadline,urgencyofw

orkflow

managementandgridstate

Assuncao[72]:Optimizeresourceutilizationandloadbalanceacrossfederatedgrids

ABRMAS[73]:Ana

lyzeagentbasedmechanismofresourcediscoveryforinsufficient

budgetedusers

GridSim[74]:Imp

lementasimulationenvironmentsuitableforutilitybasedgrid

computing

Sim[75]:Determineappropriateamountofrelaxationinnegotiatio

ncriteriato

maximizeutilityandsuccessrate

Li[76]:Analyzetim

eandlearningbasednegotiationstrategiesfora

daptingwith

dynamicgrid

Ghoshetal.[77]:H

arnesscomputingpowerinmobiledevicesthroughanefficient

pricingstrategyto

allocatejobsonthem

Sim[78]:Studybargainingmodelsbyconsideringgriddynamicsan

dappropriate

relaxationofbarga

iningterms

Proport

iona

lshare

base

dauctiond:

Economicefficiency,scalability

Tycoon[79]:Allocatehostsefficientlyinacluster.Developanagentbasedapproach

toallocateresources

Xavieretal.[80]:M

anageresourceloadondistributedlargescalein

frastructureby

controllingresourcepricesacrossagridnetwork

Proport

iona

lresourceshare:Inthismechanism,thepercentageofthe

resourceshareallocationtotheusersapplic

ationisproportionaltothe

bidvalueincomparisontotheotherusersb

ids

Lessjobcancela

tionrate

OSEP[81]:Allocateresourcesfairlythroughownershareenforcementpolicyand

distributedownershipconcept

Gomoluch[61]:StudyandcompareDAandproportionalresources

hareintermsof

systemload,heterogeneityandcommunicationdelay

Li[82]:Applyagen

ttechnologytomaximizejobaccomplishmentratewhile

minimizingthecostaccrued

Libra[83]:Sharedeadlineandbudgetstrategicallybyconsideringu

serutilityrather

thansystemperformance

Firstpricesea

ledbidauct

ione:

Resourceallocationefficiency,global

allocationefficiency

Mirage[84]:Deplo

ytestbedresourcestocomputingusersinanagg

regatedmanner

throughcombinatorialauction


8/14


Tab

le1(continue

d)

Economicmodel

Strengths

Proposedby:resea


Contractnetprotoco

l(CNP):Accordingtothismodel,auseriscalleda

managerandaprovideriscalledacontracto

r.Here,amanagerdeclares

his/herrequirementsandinvitesbidsfroma

vailablecontractors.

Interestedcontractorsevaluatethedemandsandrespondbysubmitting

theirbids.Themanagerevaluatesthebidsandselectsacontractorto

proceed

Utilitybasednegotiation,scalability,

resourcecooperation,meta-scheduling

Chaoetal.[85]:Gr

oupgridnodesintermsoftheirrespectivedesirestooptimize

systemperformance

Caramia[86]:Optimizesystemperformancethroughnegotiatingdistributed

schedulers

Goswami[87]:Maximizesuccessratewhileminimizingtimeandcostconstraintsat

differentjobarriva

lperiods

Stefano[88]:Optim

izeQoSbyadoptingCDNfconceptingrid

Ranjanetal.[89]:OptimizeQoSandresourceallocationdecisionsb

ySLAbasedsuper

schedulinginfederatedgrids

Dominiaketal.[90

]:ImplementCICgtofacilitateformingteamsofdifferent

provisioningandspecialization

Paurobally[91]:Developmulti-agentnegotiationtechniquestofacilitatebuildingan

adaptiveandautonomousgrid

Ouelhadjetal.[92]:DesignSLAbasednegotiationtodealwithunce

rtaintiesin

resourcecooperation,systemflexibilityandscalability

Juhasz[93]:Study

systemperformanceintermsofsystemsize,agentloadand

deadline

Poojaetal.[94]:Proposemulti-agentbasedhierarchicalbiddingmechanismto

meta-schedulegridservices.Supportre-negotiationamongagentssubjectto

uncertaintyofjobexecution

a

Makecontractstoobtaintherightsofbuying/sellingaparticularservicewithinaspecificperiodandataspecificprice.

b

TrustbasedContinuousDoubleAuction:suppor

tsagentstocommittotradestheytrust.

c

ApplicationLayerNetwork:hidestheheterogen

eityofaservicenetworkfromusersview.

d

ThisisliketheEnglishauction,except,afterthe

auctionprocess,resourcesaresharedamongthe

participantsaccordingtotheirbids.

e

Anumberofuserssubmittheirbidsonlyoncetogetaservice,withoutknowingotherbids.Theh

ighestbidderwinstheserviceatthepricehe/shebids.

f

ContentDistributionNetwork:duplicateswebresources(ownedbythesameorganization)from

anoriginservertodifferentreplicaservers.

g

ClientInformationCenter:storesinformationso

thatauseragentcaninteractandnegotiateonp

re-executionentities(e.g.price,QoS)orjoinana

gentteam.


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is suitable for optimizing QoS related to jobs and maximizingrevenue for providers. Additionally, it is suitable for efficientresource allocation. The Bargaining model and CNP support utilitybased negotiation. CNP can further help in cooperating distributedresources and maintaining scalability. The Proportional sharebased auction model is suitable for economical efficiency andrevenue, whereas the Proportional resource share model decreasesthe job cancelation rate. The First price sealed bid auction modelprovides a globally efficient resource allocation.

Apart from this, Neumann et al. [2] mainly identify twomodes of applications: batch3 and interactive4. They furtherdistinguish grid markets in terms of application dependencywhich considers complex services and application independencywhich considers only physical resources. They propose differenteconomic models for different mode of applications. For example,for Batchmode underapplication-dependent market, they proposeeither multi-attribute combinatorial auction or proportional sharebased auction and for the same mode under an application-independentmarket,they propose a bargaining protocol.However,their proposed market mechanisms for different classes are just ahypothesis and not based on any experimental proof. Applicabilityof a market mechanism in a large scale distributed environment

requires extensive study on the mechanism with real parameters.In this paper, we focus only on the models that have already beenstudied extensively by the researchers using real parameters.

In the literature, we also find that one model is being comparedto another model using various criteria. Table 2 describes this indetail: the first column presents different economic models, thesecond column shows the models that are being compared and thelast column shows the different features used for comparison:

Table 2 explains different economic models in the grid andcompares them with one another in terms of various criteriasuch as market equilibrium, handling a large number of users anduser and provider efficiency. One of the widely proposed models(according to Table 1), the Commoditymarket model is better thanthe English auction model in terms of having less complexity in

selecting a market to participate, handling large number of usersand maintaining market equilibrium. The Commodity marketmodel is more efficient in managing time and handling largenumber of users than the Dutch auction model. The Commoditymarket model is also more suitable than the Flat pricing modelfor resource allocation efficiency, time efficiency and scalability.Another widely proposed model, the DA, is better than the Dutch,English, First price sealed bid and Vickrey auction models in termsof maintaining market equilibrium, broadcasting overhead andachieving user and provider efficiency. The DA has better marketequilibrium, user provider efficiency and price stability comparedto the Proportional share model. The DA is also better than anotherpopular model, the Commodity market, in terms of time efficiencyand decentralization. The English auction (multi attribute) model is

betterthan theFlat pricing andUnit (fixed) pricing models in termsof QoSand economic efficiency. The multi attributeEnglish auctionmodel is better than the single attribute English auction model intermsof QoS optimization and consideration of combinatorial bids.The English auction model (multi attribute) is better than the Firstprice sealed bid auction, Vickrey auction model and DA modelsin terms of QoS, revenue and economic efficiency. Even thoughthe Commodity market model is one of the widely proposedmodels, it is less efficient to evaluate the true market value of aresource compared to the English auction (multi-attribute) model.

3 Planned execution time and expected termination time for this type of

applications are possibly known in advance.

4 Planned execution time and expected termination time for this type ofapplications are usually known in advance.

The Proportional share based auction model is better than theEnglish and the Vickrey auction models in terms of scalability andresource allocation efficiency.

By analyzing Tables 1 and 2, it can be seen that even thoughthe economic models play an important role in grid computing,a particular model is not suitable for all the scenarios in a gridenvironment. In addition, due to the dynamic nature of the grid,the application of a single model might not be able to harnessthe full potential from the grid. However, in the literature, thereis no such mechanism of combining two or more economicmodels to utilize the strengths of multiple economic models indifferent scenarios. For example, there is no mechanism thatchooses the Commodity market model for market equilibrium andswitches to the auction model to give more profit to providers.However, managing more than one model in a highly dynamicand heterogeneous environment poses another challenge. Thefollowing section summarizes future directionsthat couldbe takenbased on our findings that would ultimately help in building arobust and viable market-based grid environment.

4. Discussion and future research directions

Since the initiation of grid computing, a number of economicmodels have been proposed for grid computing. However, notall the models are suitable for all scenarios. Through numerousresearches, experiments and simulations, only a few of them havebeen shown to be effective in grid environment. In addition, onemodel is different from another due to its distinct features andobjectives of usage. Fig. 3 summarizes different economic modelsthat have been proposed over the years for usage in the grid.

The adoption of economic based approaches started mainly atthe beginning of this decade. Hence, Fig. 3 shows papers from2000 onwards. Fig. 3 indicates that the significance of economicmodels is increasing every year. Adoption of the Commoditymarket model started in 2000 and continues until 2010 with agap in 2002 and 2003. Many of the papers on the Commodity

market model have been published in 2007, which is quite recent.The Commodity market model has the potential of maintainingequilibrium between supply and demand, and it is economicallyefficient. This provides an incentive to resource providers tocontribute their resources in grid. The Double Auction model (DA)is another widely proposed model since 2003. In 2009, DA isthe most frequently proposed model. The DA has become morepopular especially due to its ability in handling a large numberof participants, while producing less communication overhead.However, the DA is not that economically efficient compared tothe Commodity market model or the English auction model. TheEnglish auction model achieves popularity in the grid due to itsefficient resource allocation and economic efficiency. Hence, theEnglish auction model has been continuouslyproposed since 2004.

However, the English auction model is not suitable for handlinga large number of users and is not decentralized. The Bargainingmodel has been proposed since 2005 and became popular, sinceit supports negotiation among grid participants, which assiststo form utility based computing. In spite of the utility basednegotiation, the Bargaining model is not so economically efficientand produces high communication overhead. The next threemodels, proportional share based auction, proportional resourceshare andfirstpricesealedbid auction could notachieve that muchpopularity in the grid. Only a few papers have proposed thesemodels across the years. Among these three models, proportionalresource share is the most discussed, since it supports fairnessamong theresources. Finally, Contract NetProtocol(CNP)has somepotential to meet the vision of large scale resource collaboration,

since it supports cooperation among different grid organizationsto optimize resource QoS. However, cooperation among the


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Table 2

A comparative view among different economic models in grid computing.

Economic model Compared model Features

Commodity market[24,33,43]

English auction [68]

Commodity market model is:

less complex for selecting a market to

participate

more efficient in dividing the budget, if users

want to explore different markets

more time efficient more efficient in handling large number of

users more suitable for price stability

more suitable for retaining market equilibrium

more suitable for increasing user provider

efficiency

more scalable

English auction model is:

able to evaluate market price

Dutch auction: The auctioneer begins with a high price for a particular

service, which is lowered until (a) some users are willing to accept the

auctioneers price or (b) the providers minimum demand is met


more time efficient

more efficient in handling large number of

users

Vickrey auction, [43] Proportional resource share Vickrey auction: This is

very similar to the first price sealed bid auction model, except the

highest bidder wins at the price of the second highest bidder


more suitable for price stability

more suitable for retaining market Equilibrium increases user provider efficiency

Vickrey auction model is:

more suitable for handling huge

communication demand

Flat pricing (deadline and budget based)


more efficient for resource allocation

more time efficient

more scalable

Double auction [55,60,53]

Dutch auction, English auction, First price sealed bid auction, Vickrey

auction

Double auction model:

is more suitable for retaining market

Equilibrium

is more efficient for resource allocation

produces less broadcasting overhead

is more time efficient is more suitable for handling large number of

users is more suitable for price stability

is more suitable for increasing user and

provider efficiency

is more efficient for global resource allocation

English auction model is:

more suitable to optimize QoS

more efficient for maximizing revenue for

providers

better for economical efficiency

Proportional resource share

Double auction model is:

more suitable for price stability more suitable for retaining market Equilibrium

more suitable for increasing user and provider

efficiency

Commodity market

Double auction model is:

more time efficient

more decentralized

English auction (multi attribute) [63,64]

Flat (fixed) pricing, Unit pricing

English auction model (multi attribute) is:


better for economic efficiency

English auction


more suitable for considering combinatorial

bids


First price sealed bid auction, Vickrey auction


more suitable to optimize QoS more efficient for maximizing revenue forproviders

better for economic efficiency

Proportional share based auction English auction, Vickrey auction

Proportional share based auction model is:

(continued on next page)


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Table 2 (continued)

Economic model Compared model Features

more scalable more suitable for efficient resource allocation

Proportional resource share [83] PBS (Portable Batch System), FIFO (First in First out)Proportional resource share model has:

lower job cancelation rate

Fig. 3. Adoption of economic models as per year.

organizations would be a complex undertaking in the grid dueto their distinct administrative rules and policies. In addition,the CNP provides incentives to users through optimizing theirutility entities such as time, QoS, and budget, but does not providesufficient motivations for providers to achieve their goals. In 2008,most papers proposed the CNP model.

All economic models can be broadly categorized into two

approaches: Commodity and Auction market. In the Commoditymarket, where price works as the main actor to regulate marketbehavior, it can be adapted to satisfy grid users [95,33]. However,price volatility in such a market is also anticipated as a harmfulcatalyst, since it might degrade the users QoS [28]. To avoidprice volatility, different hedging strategies are proposed, thoughconstructing a hedging portfolio by the contract issuing service forindividuals basedon current market conditions obviously deservesmeaningful consideration due to the higher level of uncertaintyinvolved in the system. On the other hand, different auctions aresuitable for the distributed pricing environment. Auction modelscannot always guarantee market efficiency and thus it is difficultto maintain the consistency in supply and demand. It has alsobeen identified that an individual auction model is not suitable

to construct a precise and complete solution for a large scaledistributed system [95].

Thoughthe demandfor economic modelsincreases over time ingrid computing, an individualmodel cannot provide all the benefitsin different scenarios. Hence, we propose the following possibleresearch directions for market based grid computing.

First, we propose a comprehensive framework that couplessuitable economic models proposed for the grid and is ableto switch from one model to another to cope with differentscenarios. For example, if a grid provider runs a Commoditymarket model for market equilibrium, it can switch to a DA, oncethe number of users is increased, since the DA is suitable forthis scenario. The framework could switch back to the previousmodel, if the provider wants to receive an equilibrium between

supply and demand or notices that his/her available resources arediminishing. If a provider wants to contribute his/her resources in

a federated grid, he/she can switch to CNP or such an equivalentapplication environment that supports cooperation of resourcesamong different providers. Through a switching mechanism, gridproviders would get sufficient motivations to contribute theirresources on the grid, since it would help to cope with theheterogeneity and the dynamic nature of the grid and helpto make a considerable amount of profit through utilizing idle

resources. However, due to the extreme dynamicity involvedin grid computing and considerable level of autonomy requiredto form some special features (e.g. determine spot price ornegotiation over entities) associated with the economic models,integrating some intelligent behavior in the environment couldbe beneficial. Switching between models also need to be happenautomatically based on current scenario, preferences (e.g. budgetoptimization, social welfare), which are usually predefined by gridusers or providers. Switching must also be conducted without anyconsiderable delay.

Second, we are proposing the agent technology to be integratedin grid computing. Agents are able to sense a particular scenarioand switch to a particular model that suits the scenario bestwithout any human intervention, since agents are distributed in

nature and autonomous and intelligent in behavior. The efficiencyof agent technology in grid computinghas already been discovered,since both of them aim to achieve a large scale open distributedsystem [96]. In addition, agents can make crucial decisions (e.g. re-track routing) during unpredicted failures (e.g. network failure)without any considerable delay in order to optimize systemutilization.

Finally, our proposed agent based switching model might notonly be useful for grid computing, but also in other conventionaldistributed systems that aim to collaborate computer and internetservices over the Internet (such as cluster computing, utilitycomputing and cloud computing). In general, all of these othertechnologies have evolved under the support of grid computinginfrastructure [97]. Due to the improvement of technologies and

inexpensiveness of resources, it has been easier for the researchcommunity to define them distinctively. Cloud computing is the


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most recentamongthem andcan be differentiated from others dueto its focus on additional scalability, dynamic configuration andvirtualized services with a greater pace. According to Foster et al.,cloud computing is driven by economies of scale, that is, achievinga cost advantage through large scale resource collaboration [97].Economic models can also support tradeoff decisions (e.g. betweentime and cost) and resolve risks regarding investing huge amountsof money for buying and installing real servers [98]. From a userspoint of view, this cost reduction would help them to get low costproviders. As a result, the Provider community would be morecompetitive than might otherwise be expected. Hence, economicmodels need to be configured in such a way they can be adaptiveto the highly dynamic nature of cloud computing. Hence, ourproposed switching framework might be beneficial for the cloudcommunity as well.

5. Conclusions

Grid computing uses large scale resource collaboration. Build-ing models to fulfill this collaboration requires considerable moti-vation from resource providers. Economic models are proposed forthis purpose. Different economic models have been proposed over

time for thegrid. We conductedan extensive surveyon these mod-els and presented their strengths and weaknesses in different sce-narios as identified by grid researchers. We observed that differentmodels are suitable for different scenarios and provided a compar-ison of their performance under these scenarios. We indicated thepossibility of switching between models to maximize benefits forproviders and /or users and highlighted the use of agents for dy-namic switching. Finally, we discussed the feasibility of using thisswitching framework in paradigms such as cloud computing.

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AminulHaque is currentlystudying hisPh.D. degree at theSchool of IT, Monash University, Malaysia. He completedhis B.Sc. in Physics from Shah-Jalal University of Scienceand Technology, Bangladesh. His area of researchinterestsare: Grid computing and Agent technology.


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Saadat M. Alhashmi is a Senior Lecturer at the Schoolof Information Technology, Monash University, Malaysia.He holds a B.Sc. in Engineering from AMU, India, anM.Sc. in Automatic Controls and Systems Engineeringfrom The University of Sheffield and a Ph.D. fromSheffield Hallam University, UK. His area of researchinterestsare: SupplyChain Management, FuzzyLogic, GridComputing, Multimedia Information Retrieval and Multi-agent Methodology.

Rajendran Parthiban is a Senior Lecturer at the School ofEngineering, Monash University, Malaysia. He completedhis BE (Hons) in 1997 and Ph.D. in 2004 both fromUniversity of Melbourne, Australia. His research andprofessional interests are in the cost comparisons ofvarious optical network architectures, energy calculationsof future optical networks, security and localization inwireless sensor networks, cost-effective applications ofRadioFrequencyIdentification(RFID)and Gridcomputing.

economic model in grid computing

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