Cost-based Scheduling of Scientific Workflow Applications on Utility Grids

Transcription

1 Cost-based Schedulng of Scentfc Workflow Applcatons on Utlty Grds Ja Yu, Rakumar Buyya and Chen Khong Tham Grd Computng and Dstrbuted Systems Laboratory Dept. of Computer Scence and Software Engneerng The Unversty of Melbourne, VIC 3 Australa {ayu, ra}@csse.unmelb.edu.au Dept. of Electrcal and Computer Engneerng Natonal Unversty of Sngapore Kent Rdge Crescent, Sngapore 96 eletck@nus.edu.sg Abstract over the last few years, Grd technologes have progressed towards a servce-orented paradgm that enables a new way of servce provsonng based on utlty computng models. Users consume these servces based on ther QoS (Qualty of Servce) requrements. In such pay-per-use Grds, workflow executon cost must be consdered durng schedulng based on users QoS constrants. In ths paper, we propose a cost-based workflow schedulng algorthm that mnmzes executon cost whle meetng the deadlne for delverng results. It can also adapt to the delays of servce executons by reschedulng unexecuted tasks. We also attempt to optmally solve the task schedulng problem n branches wth several sequental tasks by modelng the branch as a Markov Decson Process and usng the value teraton method. I. INTRODUCTION Utlty computng [3] has emerged as a new servce provson model and ts servces [] are capable of supportng dverse applcatons ncludng e-busness and e- Scence over a global network. Users consume the servces when they need to, and pay only for what they use. In the recent past, provdng utlty computng servces has been renforced by servce-orented Grd computng [][] that creates an nfrastructure enablng users to consume utlty servces transparently over a secure, shared, scalable and standard world-wde network envronment. Many Grd applcatons such as bonformatcs and astronomy requre workflow processng n whch tasks are executed based on ther control or data dependences. As a result, a number of Grd workflow management systems wth schedulng algorthms have been developed by several proects such as Condor DAGMan [], Askalon [], GrADS [8], ICENI [6], APST [3], and Pegasus [4][9]. They facltate the executon of workflow applcatons and mnmze ther executon tme on Grds. However, for mposng workflow paradgm on utlty Grds, executon cost must also be consdered when schedulng tasks on resources. For a utlty servce, prcng s dependent on the level of QoS offered such as the processng speed of the servce. Typcally, servce provders charge hgher prces for hgher QoS. Therefore, users may not always need to complete workflows earler than they requre. Instead, they prefer to use cheaper servces wth lower QoS that are suffcent to meet ther requrements. Gven ths motvaton, we present a cost-based workflow schedulng algorthm for tme-crtcal workflow applcatons. The obectve functon of the proposed schedulng algorthm s to develop a workflow schedule such that t mnmzes the executon cost and yet meets the tme constrants mposed by the user. In order to solve schedulng problems effcently for large-scale workflows, we partton workflow tasks and generate a workflow executon schedule based on the optmal schedules of the task parttons. Schedulng based on workflow parttons also allows the scheduler to re-compute some partal workflows durng the workflow executon, when ther ntal schedules are volated. A deadlne assgnment strategy s also developed to dstrbute the overall deadlne over each task partton. We also attempt to solve optmally the schedulng problem for sequental tasks by modelng the branch partton as a Markov Decson Process (MDP) [], whch has proven to be effectve for modelng decson problems. Several works have been proposed to address schedulng problems based on users deadlne constrant. Nmrod-G [6] schedules ndependent tasks for parameter-sweep applcatons to meet users deadlne. In contrast, the schedulng algorthm developed n the paper ams to schedule tasks wth certan dependences. A market-based workflow management system [3] locates an optmal bd based on the assgned deadlne of each ndvdual task. However, n most stuatons users may only want to specfy a deadlne for the entre workflow executon. Therefore, we focus on how to assgn subdeadlnes of tasks to meet the overall deadlne. Proposed workflow schedulng approach can be used by both end-users and utlty provders. End users can use the approach to orchestrate Grd servces, whereas utlty provders can outsource computng resources to meet customers servce-level requrements. The remander of the paper s organzed as follows. Secton II provdes an overvew of the workflow management system. We descrbe our workflow schedulng approach n Secton III. Expermental detals and smulaton results are presented n Secton IV. Fnally, we conclude the paper wth drectons for further work n Secton V. II. WORKFLOW MANAGEMENT SYSTEM Fgure shows the archtecture of the workflow management system. Users frst submt workflow

2 specfcatons wth ther QoS requrements. The system then dscovers approprate servces for processng the workflow tasks and schedules the tasks on the servces. There are three maor steps n workflow schedulng: performance estmaton, workflow plannng and workflow executon wth run-tme reschedulng. Workflow Management System Contract Volaton Workflow Specfcaton Servce Dscovery Performance Estmator Workflow Plannng Advance Reservaton QoS Montor Executor Workflow Executon Workflow Schedulng QoS Request ReservatonRequest(SLA) ServceRequest(SLA) Feedback GSP: Grd Servce Provder SLA: Servce Level Agreement Grd Market Drectory Grd Servce Grd Servce Grd Servce Fg.. Workflow management system archtecture. In order to plan n advance, the scheduler needs to predct task executon tme on the avalable servces. Dfferent performance estmaton approaches can be appled to dfferent types of utlty servce. We classfy exstng utlty servces as ether reservaton-enabled resource or applcaton servces. Resource servces provde proportons of hardware resources, such as computng processors, network resources, storage and memory, as a servce for remote clent access. Applcaton servces allow remote clents to use ther specalzed applcatons. To submt tasks to resource servces, the scheduler needs to determne the number of resources and duraton requred to run tasks on the dscovered servces. The performance estmaton for resource servces can be acheved by usng exstng performance estmaton technques (e.g. analytcal modelng [8], emprcal [8] and hstorcal data [4][9]) to predct task executon tme on every dscovered resource servce. Unlke resource servces, a reservatonenabled applcaton servce s capable of provdng estmated servce tmes based on the metadata of users servce requests []. As a result, the task executon tme can be obtaned by the applcaton provders. Workflow plannng s to select a servce and executon tme slot for every task n the workflows based on users QoS constrants, capablty and avalablty of the servces. The reservaton manager makes reservatons n advance to ensure the avalablty of the desred tme slots for task executon. Canddate tme slots are also generated durng plannng tme for alternatve reservatons when a desred tme slot s not avalable at the tme of reservaton. At workflow executon tme, the contract between a servce provder and the workflow management system may be volated by many reasons such as resource falure. Therefore, reschedulng s deployed n the system to adapt to servce GSP Marketplace dynamcs and update the schedule. For example, f the desred start tme of a task s delayed, the scheduler wll adust the reservaton schedule for ts unexecuted chld tasks to compensate the delay. III. A COST-BASED WORKFLOW SCHEDULING The processng tme and executon cost are two typcal QoS constrants for executng workflows on pay-per-use servces. The users normally would lke to get the executon done at lowest possble cost wthn ther requred tmeframe. In ths secton we present a cost-based workflow schedulng methodology and algorthm that allows the workflow management system to mnmze the executon cost whle delverng results wthn a certan deadlne. ) Problem Descrpton and Methodology We model workflow applcatons as a Drected Acyclc Graph (DAG). Let be the fnte set of tasks ( n). T ( T Let be the set of drected arcs of the form T, ) where T s called a parent task oft, and T the chld task oft. We assume that a chld task cannot be executed untl all of ts parent tasks are completed. Let D be the tme constrant (deadlne) specfed by the users for workflow executon. Then, the workflow applcaton can be descrbed as a tuple (,, D). In a workflow graph, we call a task whch does not have any parent task an entry task denoted as T and a task whch does not have any chld task an ext task denoted as T ext. Let m be the total number of servces avalable. There are a set of servces S : cond ( n, m, m m) s capable of executng the taskt, but only one servce can be assgned for the executon of a task. Servces have vared processng capablty delvered at dfferent prces. We denote t as the sum of the processng tme and data transmsson tme, and entry c as the sum of the servce prce and data transmsson cost for processng T on servce S. The schedulng problem s to map every T onto some S to acheve mnmum executon cost and complete the workflow executon wthn the deadlne D. We solve the schedulng problem by followng the dvde-and-conquer technque and the methodology s lsted below: Step. Dscover avalable servces and predct executon tme for every task. Step. Group workflow tasks nto task parttons. Step 3. Dstrbute users overall deadlne nto every task partton. Step 4. Query avalable tme slots, generate optmzed schedule plan and make advance reservatons based on the local optmal soluton of every task partton.

3 Step 5. Start workflow executon and reschedule when the ntal schedule s volated at run-tme. We provde detals of steps -5 n the followng subsectons. The servce dscovery can be done by queryng a drectory servce such as the Grd market drectory [4]. B. Workflow Task Parttonng We categorze workflow tasks to be ether a synchronzaton task or a smple task. A synchronzaton task s defned as a task whch has more than one parent or chld task. In Fgure 7a, T, T and T 4 are synchronzaton tasks. Other tasks whch have only one parent task and chld task are smple tasks. In the example, T T9 and T T3 are smple tasks. T (a) Before parttonng. (b) After parttonng. Fg.. Workflow task partton. Let a branch be a set of nterdependent smple tasks that are executed sequentally between two synchronzaton tasks. For example, the branches n Fgure b are { T, T3, T4}, { T, T 5 6}, { T } 7, { T, T 8 9}, { T } and { T, T 3}. We then partton workflow tasks nto ndependent branches ( k) and synchronzaton tasks ( l), B such that k and l are the total number of branches and synchronzaton tasks n the workflow respectvely. LetV be a set of nodes n a DAG correspondng to a set of task parttons V ( k + l). Let E be the set of drected edges of the form V, V ) of T 8 Smple task Synchronzaton task V and T T 5 T 7 T 9 ( where V s a parent task partton V s a chld task partton of V. Then, a task partton graph s denoted as G ( V, E, D). A smple path (referred to as path) n G s a sequence of task parttons such that there s a drected edge from every task partton (n the path) to ts chld, where none of the vertces (task parttons) n the path s repeated. A task partton V has four attrbutes: ready tme ( rt [ V ] ), deadlne ( dl V ] ), expected executon tme ( eet V ] ). The [ T 3 T 6 T T earlest ready tme of V s the earlest tme the frst task n t can be executed and t can be computed accordng to ts parent parttons, rt [ V ] = max dl[ V ], where P s the set of V P parent task parttons of V. The attrbutes are related as: eet [ V ] = dl[ V ] - rt [ V ]. T 4 T T 4 T 3 T T 8 Branch T T 3 T 5 T 7 T 9 T T 6 T Y [ T T 4 T 3 T 4 C. Deadlne Assgnment After workflow task parttonng, we dstrbute the overall deadlne between each V n G. The deadlne dl [ V ] assgned to any V s a sub-deadlne of the overall deadlne D. In ths paper, we consder the followng deadlne assgnment polces: P. The cumulatve sub-deadlne of any ndependent path between two synchronzaton tasks must be same. A synchronzaton task cannot be executed untl all tasks n ts parent task parttons are completed. Thus, nstead of watng for other ndependent paths to be completed, a path capable of beng fnshed earler can be executed on slower but cheaper servces. For example, the deadlne assgned to T, } s the same as T } n Fgure 7. Smlarly, deadlnes { T 8 9 { 7 {, T3, T4 assgned to T }, T, }, and { T }, { T },{ T, T3 }} are same. { T 5 6 { 7 P. The cumulatve sub-deadlne of any path from V T V ) to V T V ) s equal to the overall ( entry ( ext deadlne D. P assures that once every task partton s computed wthn ts assgned deadlne, the whole workflow executon can satsfy the user s requred deadlne. P3. Any assgned sub-deadlne must be greater than or equal to the mnmum processng tme of the correspondng task partton. If the assgned sub-deadlne s less than the mnmum processng tme of a task partton, ts expected executon tme wll exceed the capablty that ts executon servces can handle. P4. The overall deadlne s dvded over task parttons n proporton to ther mnmum processng tme. The executon tmes of tasks n workflows vary; some tasks may only need mnutes to be completed, and some others may need at least one hour. Thus, the deadlne dstrbuton for a task partton should be based on ts executon tme. Snce there are multple possble processng tmes for every task, we use the mnmum processng tme to dstrbute the deadlne. We mplemented deadlne assgnment polces on the task partton graph by combnng Breadth-Frst Search (BFS) and Depth-Frst Search (DFS) algorthms wth crtcal path analyss to compute start tmes, proporton and sub-deadlnes of every task partton. D. Plannng The plannng stage generates an optmzed schedule for advance reservaton and run-tme executon. The scheduler allocates every workflow task to a selected servce such that they can meet users deadlne at the lowest possble executon cost. In general, mappng tasks on dstrbuted servces s an NPhard problem. To model the entre workflow as an optmzaton problem wll produce large schedulng overhead, 3

4 especally for the problem wth two dmenson constrants such as tme and cost. Therefore, we solve the workflow schedulng problem by dvdng the entre problem nto several task partton schedulng problems. After deadlne dstrbuton, we can fnd a local optmal schedule for each partton based on ts sub-deadlne. If each local schedule guarantees that ther task executon can be completed wthn the sub-deadlne, the whole workflow executon wll be completed wthn the overall deadlne. Smlarly, the result of the cost mnmzaton soluton for each task partton leads to an optmzed cost soluton for the entre workflow. Therefore, an optmzed workflow schedule can be easly constructed by all local optmal schedules. There are two types of task parttons: synchronzaton task and branch partton. The schedulng solutons for each type of partton and the overall algorthm are descrbed n followng sub-sectons. ) Synchronzaton Task Schedulng (STS) For STS, the scheduler only consders one task to decde the servce for executng that task. The obectve functon for schedulng of a synchronzaton task Y s: mn, where m and t eet Y ) c The soluton to a sngle task schedulng problem s smple. The optmal decson s to select the cheapest servce that can process the task wthn the assgned sub-deadlne. ) Branch Task Schedulng (BTS) If there s only one smple task n a branch, the soluton for BTS s the same as STS. However, f there are multple tasks, the scheduler needs to make a decson on whch servce to execute each task after the completon of ts parent task. The optmal decson s to mnmze the total executon cost of the branch and complete branch tasks wthn the assgned subdeadlne. The obectve functon for schedulng branch B s: k k mn c, where k m and t eet( B ) T B T B BTS can be acheved by modelng the problem as a Markov Decson Process (MDP) [], whch has been shown to be effectve for solvng sequental decson problems. 3) MDP Model for Sequental Branch Tasks The defnton of our MDP model for schedulng branch B s descrbed below: States: A Markov decson process s a state space S such that: Defnton : A state s S conssts of current executon task, ready tme RT and current locaton. Actons and transtons: For every state s, there s a set of actons A s. Actons ncur mmedate utlty and affect the MDP to transt from one state to another. ( Defnton : An acton n the MDP s to allocate a tme slot on a servce to a task. There are two varables assocated wth each acton a : nput data transmsson tme plus the processng tme of the servce denoted as t and transmsson cost plus the servce cost denoted as c. Defnton 3: u (s,a,s') s the mmedate utlty obtaned from takng acton a at state s and transtonng to state s'. u (s,a,s') = Defnton 4: A transton ncurred by an acton from one state to another s determnstc, as servces are utlty servces and can be reserved n advance. * The MDP problem s to fnd an optmal polcy π for all possble states. A polcy s a mappng from s to a. Decson makng for fndng an optmal acton for each state s not based on the mmedate utlty of the acton but ts expected utlty, whch s the sum of all the mmedate utltes obtaned as a result of decsons made for transtng from ths state to a termnal state. The value assocated to each state represents the expected utlty of ths state n the MDP. Ths value s calculated recursvely by usng the value of successor states. The value of one state s s: U ( s) = mn{ u( s, a, s') + U ( s')} a As The best acton for state s s: * π ( s) = arg mn{ u( s, a, s') + U ( s' )} a As, s'. RT > sub-deadlne 4) Schedulng Algorthm Algorthm shows the pseudo-code of the algorthm for plannng an executon schedule. After acqurng the nformaton about avalable servces for each task, a task partton graph G s generated from the applcaton graph and overall deadlne D s dstrbuted over every partton n t. Then optmal schedules are computed for every partton n G level-by-level usng ether STS or BTS. We also found that after the optmzaton of one partton, there s an dle tme between ts expected completon tme and assgned suba.c, otherwse The optmal polcy ndcates the best servces that should be assgned to execute branch tasks under a specfc subdeadlne. The computaton of the optmal polcy can be solved by usng a standard dynamc programmng algorthm such as polcy teraton and value teraton [] (we have used value teraton here). Value teraton computes a new value functon for each state based on the current value of ts next state. Value teraton proceeds n an teratve fashon and can converge to the optmal soluton quckly. The complexty analyss of value teraton can be found n [5]. By usng dynamc programmng, we can also record a number of canddate solutons whle fndng the optmal polcy. Therefore, once the optmal tme slot s reected or not avalable, the scheduler can make another reservaton mmedately by usng second optmal slot. 4

5 deadlne. Instead of watng, we adust the assgned subdeadlnes of optmzed parttons and the ready tmes of ther chld parttons. E. Reschedulng Run-tme reschedulng s developed to adapt to dynamc stuatons such as delays of servces and varatons n avalablty of servces due to falures, n order to complete workflows and satsfy users requrements. The key dea of our reschedulng polcy for handlng an unexpected stuaton s to re-adust sub-deadlnes and re-compute optmal schedules for unexecuted task parttons. We also consder reschedulng the mnmum number of tasks, snce the scheduler need to cancel earler reservatons for tasks that need to be rescheduled to other servces. Therefore, the scheduler re-computes unexecuted task parttons level-bylevel. For example, f the executon of one task partton s delayed, the scheduler looks at ts chld task parttons. If the delay tme can be accommodated by the chld task parttons, reschedulng wll not mpact on ts lower levels. Otherwse, the rest of the outstandng delay tme s dstrbuted further to successve task parttons of the chld parttons. In addton to handlng task executon delay, the level-bylevel task partton based approach can also be appled for managng other dynamc stuatons such as servce unavalablty and servce polcy change. Algorthm. Schedulng algorthm for cost optmzaton wthn users deadlne Input: A workflow graph (,, D) Output: A schedule for all workflow tasks request processng tme and prce from avalable servces for convert nto task partton graph G ( V,E, D) 3 dstrbute deadlne D over G V 4 Repeat 5 S get unscheduled task parttons whose parent task parttons 6 have been scheduled 7 for all S do 8 compute ready tme of 9 query avalable tme slots durng ready tme and sub-deadlne on avalable servces f s a branch then compute an optmal schedule for usng BTS 3 Else 4 compute an optmal schedule for usng STS 5 end f 6 make advance reservatons wth desred servces for all tasks n 7 adust sub-deadlne of 8 end for 9 untl all parttons have been scheduled IV. PERFORMANCE EVALUATION We use GrdSm [7][] to smulate a Grd testbed for our experments. Smulaton facltates evaluaton as the same testbed envronment can be repeated for dfferent approaches. Fgure 3 shows the smulaton envronment n whch smulated servces are dscovered by queryng GrdSm Index T Servce (GIS) and every servce s able to handle free slot query, reservaton request and commtment. We compare our proposed schedulng algorthm denoted as Deadlne-MDP wth two other schedulng approaches: Greedy- Cost and Deadlne-Level. These two approaches are derved from the cost optmzaton algorthm n Nmrod-G, whch s ntally desgned for schedulng ndependent tasks on Grds. sorts servces by ther prces and assgns as many tasks as possble to servces wthout exceedng the deadlne. Deadlne-Level frst dvdes workflow tasks nto levels (based on ther depth n the workflow graph), then dvdes the deadlne by the number of levels and dstrbute the dvded sub-deadlnes over task levels. In, the deadlnes of all tasks are the same as the overall deadlne, whereas n Deadlne-Level, tasks on the same level have the same sub-deadlne. A. query(type A) B C B 3 4 Workflow System (3) (6) (9) (5) GIS 3.servce lst. regster.regster(servce type) 4. AvalableSlotQuery(duraton) 5. slots 6. makereservaton(task ) Fg. 3. Smulaton envronment. Algn_wap reslce a. Ppelne b. Parallel applcaton (fmri workflow [6]) 5 6 c. Hybrd structure (proten annotaton workflow [5]) Grd Servce Grd Servce Algn_wap Algn_wap Algn_wap (3) reslce reslce reslce (6) softmean slcer slcer convert convert Fg. 4. Workflow applcatons. The label on the left of a task denotes the requred servce type. The number n brackets represents the length of the task n MI. slcer convert (3) (6) (3) (3) (3) (6) (6) (6) SgnalP COILS SEG PROSITE TMHMM Prospero 9 (3) Summary HMMer 7 PSI-BLAST BLAST IMPALA (3) (3) (3) PSI-PRED 3D-PSSM 8 (3) (6) (6) (3) (6) Genome Summary SCOP (5) (5) (3) (3) (3) (3) (6) (6) (5) (6) (9) 5

6 We smulate three common workflow structures n scentfc workflow applcatons for our experments: ppelne, parallel and hybrd. A ppelne applcaton (see Fg. 4a) executes a number of tasks n a sngle sequental order. A parallel applcaton (see Fg. 4b) requres multple ppelnes to be executed n parallel. For example, n Fg. 4b, there are 4 ppelnes (-, 3-4, 5-6 and 7-8) before task 9. A hybrd structure applcaton (see Fg. 4c) s a combnaton of both parallel and sequental applcatons. In our experments, we select a neuro-scence workflow [6] for our parallel applcaton and a proten annotaton workflow [5] developed by London e-scence Centre for our hybrd workflow structure applcaton. As executon requrements for tasks n scentfc workflows are heterogeneous, we use servce type to represent dfferent type of servce. Every task n our expermental workflow applcatons requres a certan type of servce. For example, task, 3, 5, 7 n parallel applcaton requres servce type Algn_wap and task, 4, 6 and 8 requres reslce. In the smulaton, we use MI (mllon nstructons) to represent the length of tasks and use MIPS (Mllon Instructons per Second) to represent the processng capablty of servces. We smulate 5 types of servces, each supported by dfferent servce provders wth dfferent processng capablty. The values of MIPS for servces range from to 5 and the value of MI for each task s ndcated n bracket next to the task n Fgure 4. In the experments, every task n the workflows generates output data requred by ts chld tasks as nputs. The data need to be staged out from the task processng node and staged nto the processng node of ts chld tasks. The I/O data of the workflows range from MB to 4 MB. The avalable network bandwdths between servces are Mbps, Mbps, 5Mbps and 4Mbps. For our experments, the cost that a user needs to pay for a workflow executon comprses of two parts: processng cost and data transmsson cost. Table I shows an example of processng cost, whle Table II shows an example of data transmsson cost. It can be seen that the processng cost and transmsson cost are nversely proportonal to ts processng tme and transmsson tme respectvely. Table I. Servce speed and correspondng prce for executng a task. Servce ID Processng Tme (sec) Cost (G$) Table II. Transmsson bandwdth and correspondng prce. Bandwdth (Mbps) Cost/sec (G$/sec) The two metrcs used to evaluate the schedulng approaches are tme constrant and executon cost. The former ndcates whether the schedule produced by the schedulng approach meets the requred deadlne, whle the latter ndcates how much t costs to schedule the workflow tasks on the testbed. Fgure 5 to 7 compare the executon tme and cost of usng Deadlne-MDP, Deadlne-Level and for schedulng ppelne, parallel and hybrd structure applcatons wth deadlne.5,,.5,, and.5 hours respectvely. It can be seen that does not guarantee that users deadlnes can be met, whereas both Deadlne-MDP and Deadlne-Level can meet deadlnes. also ncurs sgnfcantly hgher executon costs even though t takes longer tme to complete executons. Ths s because t attempts to meet the deadlne by employng faster but more expensve servces as the deadlne approaches. Completon Tme (hours) Executon Cost (G$) Ppelne Applcaton User Deadlne (hours) a. Executon tme of three approaches. Ppelne Applcaton User Deadlne (hours) Deadlne-MDP Deadlne-Level Deadlne-MDP Deadlne-Level b. Executon cost of three approaches. Fg. 5 Executon tme and cost usng three approaches for schedulng the ppelne applcaton. Completon Tme (hours) Parallel Applcaton User Deadlne (hours) a. Executon tme of three approaches. Deadlne-MDP Deadlne-Level 6

7 Executon Cost (G$) Parallel Applcaton User Deadlne (hours) Deadlne-MDP Deadlne-Level b. Executon tme of three approaches. Fg. 6. Executon tme and cost usng three approaches for schedulng the parallel applcaton. Completon Tme (hours) Executon Cost (G$) Hybrd Structure Applcaton User Deadlne (hours) a. Executon tme of three approaches. Hybrd Structure Applcaton User Deadlne (hours) Deadlne-MDP Deadlne-Level Deadlne-MDP Deadlne-Level b. Executon cost of three approaches. Fg. 7. Executon tme and cost usng three approaches for schedulng the hybrd structure applcaton. Percentage of Tasks Completed (%) Deadlne Deadlne-MDP Deadlne-Level Completon Tme (Hours) a. Percentage of tasks completed. Executon Cost (G$) Deadlne-MDP Deadlne-Level Deadlne Completon Tme (Hours) b. Executon Cost. Fg.8. Deadlne-MDP, Deadlne-Level, and schedulng for Hybrd Structure Applcaton. Both Deadlne-Level and Deadlne-MDP can meet the deadlnes, but Deadlne-MDP spends much less cost for shorter deadlnes. Deadlne-MDP can acheve that for the ppelne applcaton by modelng the entre ppelne applcaton as one MDP process, such that t can fnd an optmal path among the cheapest servces to execute tasks and transfer nput/output data. Smlarly for the parallel applcaton, t also optmzes the cost for branches wthn the parallel applcaton. For the hybrd structure applcaton, Deadlne-MDP performs better as t assgns sub-deadlnes to tasks based on ther dependences and estmated executon tmes. Deadlne- Level assgns sub-deadlnes only based on the task level and thus ncurs unnecessary cost. Ths s because parent tasks that are completed earler usng faster but more expensve servces stll need to wat for other slower parent tasks to be completed before ther chld tasks can start executon. Ths shows that t s mportant to consder task dependences as t s pontless to employ expensve servces f not requred. Percentage of Tasks Completed (%) delay mns delay 5 mns delay mns Deadlne Completon Tme (Hours) a. Percentage of tasks completed. 7

8 Executon Cost (G$) delay mns delay 5 mns delay mns Deadlne Completon Tme (Hours) b. Executon cost. Fg. 9. Deadlne-MDP schedulng wth delay of, 5, and mnutes for task 6 n Hybrd Structure Applcaton. Fgure 8 shows the percentage of tasks completed and executon cost for the hybrd structure applcaton at varous tmes. We can see that Deadlne-Level attempts to complete tasks earler by usng more expensve but faster servces. However ts fnal completon tme s only margnally lower than that of Deadlne-MDP. On the other hand, Deadlne- MDP selects much cheaper servces than Deadlne-Level but can stll meet the fnal deadlne. In contrast, chooses cheaper servces and completes tasks slowly at the early stages of the executon such that t does not have enough tme to complete the partally remanng tasks before the deadlne, even though t selects more expensve servces to speed up executon durng the fnal stage. We now evaluate the reschedulng approach n Deadlne- MDP. Fgure 9 shows how Deadlne-MDP performs wth delays of, 5 and mnutes for the executon of task 6 n the hybrd structure applcaton. For the delays of 5 mnutes and mnutes, Deadlne-MDP can stll meet the deadlne by reschedulng the partally remanng unexecuted tasks. However, the executon cost ncreases for longer delays snce the scheduler swtches the remanng tasks to more expensve servces n order to complete the remanng executon wthn the deadlne. VI. CONCLUSION AND FUTURE WORK Utlty Grds enable users to consume utlty servces transparently over a secure, shared, scalable and standard world-wde network envronment. Users are requred to pay for access servces based on ther usage and the level of QoS provded. Therefore, workflow executon cost must be consdered durng schedulng. In ths paper, we proposed a cost-based workflow schedulng algorthm that mnmzes the cost of executon whle meetng the deadlne. We also descrbed task parttonng and overall deadlne assgnment for optmzed executon plannng and effcent run-tme reschedulng. We have used a Markov Decson Process approach to schedule sequental workflow task executon, such that t can fnd the optmal path among servces to execute tasks and transfer nput/output data. The expermental results demonstrate that the proposed schedulng approach can meet users deadlne whlst spendng less cost. It can also adapt to the delays of servce executons by reschedulng unexecuted tasks to meet users deadlnes. In future, we wll further enhance our schedulng method to support multple servce negotaton models. ACKNOWLEDGMENTS We would lke to thank Hussen Gbbns, Chee Shn Yeo, Srkumar Venugopal, Sushant Goel, Tanch Ma and Arun Konagurthu for ther comments on ths paper. We also want to thank Anthony Sulsto for provdng reservaton nfrastructure n GrdSm. 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