A dynamic-balanced scheduler for Genetic Algorithms for Grid Computing

Transcription

1 A. J. Sanchez Santago, A. J. Yuste, J. E. Munoz Exposto, S. Garca Galan, J. M. Maquera Marn, S. Bruque A dynamc-balanced scheduler for Genetc Algorthms for Grd Computng A.J. SÁNCHEZ SANTIAGO (2), A.J. YUSTE (1), J.E. MUÑOZ EXPÓSITO (1), S. GARCÍA GALÁN (1), J.M. MAQUEIRA MARÍN (2), S. BRUQUE (2) (1) Telecommuncaton Engneerng Department (2) Busness Admnstraton and Accountng Department Unversty of Jaén Alfonso X El Sabo, 28. Lnares, Jaén. SPAIN {ajsanta, ajyuste, jemunoz, sgalan, maquera, sbruque}@ujaen.es Abstract. The new paradgm of dstrbuted computaton, grd computng, has gven rse to a large amount of research on resource schedulng. Unlke the dstrbuted computaton, grd computng uses heterogeneous resources, for what grd computng entals new challenges as the adaptaton of parallel algorthms before developed for homogeneous resources cluster to the dynamc and heterogeneous resources. In ths paper we present a dynamc-balanced scheduler for grd computng that solves two typcal knds of problems of grd computng, usng for them the cycles of some resources of the grd. The frst problem s based on teratve tasks that usually appear n optmzaton problems. The second problem s a drected acyclc graph (DAG) problem. Expermental results usng dynamc-balanced scheduler show that t s possble to obtan an mproved use of the resources n the grd. Ths strategy enables to adapt the length of a task to the computng capacty of each resource at any gven moment. Furthermore, ths schedulng strategy enables to execute all the tasks n a shorter tme. Key-Words: - Grd computng, dynamc-balanced scheduler, genetc algorthm, optmzaton problem. 1 Introducton Over the past few years, the development of hgh speed networks, ncreasng overall computng capacty and low cost of storage devces have gven rse to a new paradgm of dstrbuted computaton: grd computng [1]. A Grd System, fg. 1, s made up by a set of Vrtual Organzatons (VO). A VO comprses a set of ndependent organzatons that share heterogeneous resources to acheve a common msson, but that are not lmted to an allance among frms. Interacton among members of the vrtual organzaton s manly done through computer networks. VO1 SupercomputerA CentreA VO3 Gateway 1 GrdManager CentreB Fgure 1. A Grd System SupercomputerA GrdManager Gateway 2 VO2 Unlke tradtonal dstrbuted computaton, whch usually uses a geographcal lmted area, a grd system can be run throughout several organzatons that utlze heterogeneous resources n a large geographcal area. Despte of ts advantages, Grd computng also entals many new challenges, lke the adaptaton of parallel programs prevously developed for homogeneous resources clusters to the dynamc and heterogeneous Grd resources wth mnmal ntruson nto the code or resource schedulng. In a grd envronment, the scheduler s the manager of the workflow, actng as ntermedary between the user and the dstrbuted resources, thus concealng the complexty of the grd system [2]. The scheduler dentfes avalable resources, negotates and organzes ther use, decdes when to execute the tasks and eventually organzes the results. Overall, a scheduler makes a decson on when to execute a task n a gven resource. Ths assgnment s amed to maxmze (or mnmze) an objectve functon, such as the tme of executon, number of tasks not executed, executon costs, etc. Most of the recent lterature treats the length of a task as a constant [3], whch can not change durng executon. Ths assumpton for a grd, whch s a hghly dynamc envronment, can make the schedulng algorthms smple and effcent; but can undermne the schedulng algorthms applcablty n some ISSN: Issue 1, Volume 8, January 29

2 A. J. Sanchez Santago, A. J. Yuste, J. E. Munoz Exposto, S. Garca Galan, J. M. Maquera Marn, S. Bruque stuatons. At the tme of schedulng, the workload of the resources n the grd may be unknown. Furthermore, ths workload may change over tme, as the tasks start and fnsh perodcally. Ignorng such uncertan factors may result n an ncreasng number of schedulng errors. Nowadays many applcatons have been developed for tradtonal homogeneous parallel systems. Adaptng these applcatons, developed for homogeneous envronments, to dynamc heterogeneous resources entals a new challenge whch has to do wth keepng a hgh level of applcaton effcency. An adequate workload optmzaton method should take nto account two man characterstcs: (1) Applcaton characterstcs, such as memory requrements, data beng transferred between processes, applcaton communcaton structures, hard dsk or other I/O actvty, etc. (2) Resource characterstcs, such as memory of the resources and computatonal capacty, network bandwdth, dsk I/O speed, and the heterogenety level of the resources randomly assgned to the applcaton by the Grd resource broker. The workload optmzaton method should be: (a) Self-adaptng and flexble wth respect to the type of applcaton. (b) Computatonally nexpensve;.e. not to nduce a large overhead on the applcaton performance. (c) Should not requre sgnfcant modfcatons n the code. In sum, the load balancng should be dynamc and fully automated snce the complexty of the Grd envronment should be hdden from end-users. Tradtonally there are two approaches that have proved useful for workload balancng n parallel applcatons: (1) to carefully calculate the dstrbuton of the workload, takng nto account all the propertes of the envronment and applcaton (a tme and resource consumng task requrng expert knowledge of the applcaton structure and algorthms beng used); and (2) dstrbutng the workload n a straght forward way, optmzng the processng capacty of the resources. Our am, focused on computatonal grd, conssts of buldng a scheduler for a SME (Small-Medum Enterprse) that allows to use the resources dle cycles to execute tasks, whch n a local machne would take several days, whereas n a grd system can only take a few hours. We wll develop a scheduler that can be used by the company s network managers, n such a way that s not necessary for them to obtan addtonal tranng or hre new employees. Wth the scheduler we propose, we wll mnmze the overall response tme for the entre workflow, whle operaton costs reman unchanged snce the frm can use ts own dle resources to run the grd. Our scheduler splts the total workflow accordng to the dle cycles of each resource, so that all the resources can fnsh each task at the same tme. The new scheduler s easy to use, s flexble and only needs an estmaton of the process capacty of each resource as the man nput. The smulatons carred out n ths research show that our algorthm mproves others schedulers presented n pror lterature. Ths document s organzed as follows: n secton 2 we analyze recent work n grd resource schedulng. In secton 3 we descrbe the dynamc-balanced scheduler for typcal problems of grd computng. In Secton 4 we show the results of the smulaton usng [4]. Fnally, n secton 5 we present the conclusons and future drectons. 2 Background Resource schedulng for tasks n a grd envronment has been extensvely studed n recent years. Some works focus on the schedulng of a set of ndependent tasks usng schedulng technques such as genetc algorthms [5], data mnng [6] or swarm ntellgence [7-8]. Other works have studed schedulng technologes usng economc/market-based modelss [9]. More recent works consder data dependences between tasks, whch are threaded as a workflow wth transtons ndcatng drect data dependences. To mnmze the complexty of schedulng a workflow, a workflow s parttoned nto a set of subworkflows, each beng assgned to a resource. In [1], Duan et al. proposed a workflow parttonng algorthm based on a set of rules that ntend to mnmze the data flow between subworkflows. Spooner et al. [11] proposed a two level algorthm that conssted of one global scheduler and a number of local schedulers, one for each grd resource. Another research lne leaves out the assumpton that each grd resource must be a cluster. Instead, a grd resource s smply consdered as the one that s ISSN: Issue 1, Volume 8, January 29

3 A. J. Sanchez Santago, A. J. Yuste, J. E. Munoz Exposto, S. Garca Galan, J. M. Maquera Marn, S. Bruque capable of provdng servces, computaton, and/or storage. Some researches propose to determne the grd resources for all tasks n a workflow before the workflow s started, whch s referred to as statc resource schedulng n subsequent dscusson. In [12], a par of a task (t) and a grd resource (r) receves a rank whch s a lnear combnaton of computng and data transfer tme for executng t on r. Three algorthms, namely mn-mn, max-mn, and sufferage, were proposed to mnmze the makespan of the entre workflow based on the ranks. Consderng the fact that grd may be hghly dynamc, some researches propose to perform resource schedulng only for tasks that are ready to execute, referred to as dynamc resource schedulng. Blythe et al. [13] adopted mn-mn strategy for both statc and dynamc resource schedulng and concluded that the statc resource schedulng s more sutable for datantensve workflows, whle dynamc resource schedulng performs better for computaton-ntensve workflows. Wess and Pnedo [14] have shown that the SEPT (Short Expected Processng Tme) strategy, whch gves prorty to jobs wth shortest expected processng tmes, and the LEPT (Long Expected Processng Tme) strategy, whch gves prorty to jobs wth longest expected processng tmes, can be used to mnmze expected flow tme and expected makespan respectvely, when the processng tme of each job follows an exponental dstrbuton. However, n a real grd envronment, the processng tme of each task by a resource may not follow an exponental dstrbuton. In these works the length of a task from a workflow s known n advance and the optmzatons are based on an n advance predetermned workflow. Conversely, we experment n ths work wth several workflows n whch the sze of each task s dfferent. 3 The proposed algorthm The desgn space for Grd Schedulers n general s very rch. Frst, t depends on what objectve functon the user wants to mnmze or maxmze examples beng mnmzng overall job completon tme, mnmzng communcaton tme and maxmzng resource utlzaton or throughput. Second, t depends on how the job requrements, job performance models, and Grd resource models are specfed and used. The scheduler must also carefully choose between dfferent mplementatons of user authentcaton, allocaton, and reservaton. Other choces nclude schedulng applcaton components for sngle or multple users and whether reschedulng or re-plannng s requred. There are usually two types of problems n task schedulng: an optmzaton problem and a drected acyclc graph (DAG) problem, fg. 2. In ths fgure we show an example of a smple DAG wth 1 tasks. Tasks 5, 6 and 7, cannot start untl the prevous tasks 2, 3, 4 has fnshed. For the last task, 1, t cannot begn untl tasks 8 and 9 are fnshed, even f task 7 was fnshed before Fgure 2. A DAG Wth the adaptve dynamc-balanced scheduler we try to valdate two problems: on the one hand a genetc algorthm for solvng optmzaton problems whch has a set of ndependent workflows, and on the other hand a DAG problem. In both cases we have used CPU dle cycles of some resources, whch a company has n several networks, mnmzng the tme of executon of each workflow. Each task requres some data as nput and output and may be executed n one of a several resources, whch satsfy the overall objectve. 3.1 The model We develop a general model to verfy the valdty of our scheduler. Our model s made up by a set of tasks, grouped n a workflow. A workflow wll not begn next teraton unless the pror one had been fnshed. A typcal example of ths s produced n processes of optmzaton, lke genetc algorthms, n whch untl a generaton has not been fully evaluated, t s not possble to evaluate the followng generaton. In order to formulate an ntegrated model, the followng parameters are ntroduced: ISSN: Issue 1, Volume 8, January 29

4 A. J. Sanchez Santago, A. J. Yuste, J. E. Munoz Exposto, S. Garca Galan, J. M. Maquera Marn, S. Bruque Parameters: n: numbers of resources ter: numbers of teratons N: load for each teraton N : task for each resource µ : nstructons per second (MIPS) T: tme of teraton OT: overall tme of a workflow In our schedulng, we have chosen our objectve functon: mnmze overall job completon tme. It s only necessary to count the maxmum tme of executon from the worst relaton task resource. Ths tme can be worked out wth the followng mathematcal equaton (1). OT = N ter MAX = 1 µ + Ts + Tr (1) The tme of an teraton (2) s gven by the longest tme needed to execute a task n the worst avalable resource. T = N tasks MAX k= 1 µ + Ts + Tr (2) Where Ts s the tme that takes sendng a task to the resource that s gong to execute t and Tr the tme that takes recevng the result from a task. We consder the tme of sendng and recevng a task neglgble, because f ths tme s hgher than the tme of executon of the task we do not send t to the grd. Thus, the tme of one partcular teraton can be smplfed by (3): T MAX N (3) µ To mnmze ths T we assgn a proportonal load to each followng resource, ths way we do not depend on the resource that offers a worse performance: µ N = N n µ = 1 (4) Thus, the nnovaton n ths study les n that we carry out a prevous study of the dle cycles of all the resources we have, adaptng the length of a task s accordng to the total dle cycles. Wth ths proposal we obtan two results: on the one hand we carry out a better load balancng for the resources, assgnng hgher loads to the resources presentng a hgher avalablty and, on the other hand, we reduce the tme of executon of the workflows. 3.2 An executon In the followng fgures we show the process of executon of teraton n our model by a smple example. In the frst step, fg. 3, we can dentfy three resources of our Grd System. The scheduler asks the resources, by the functon Int_Job(I_J), for the µ dle that they have. Every resource sends to the scheduler the number of dle cycles that t has avalable for ths teraton. R1 R R3 I_J MIPS 298 MIPS 3128 MIPS Sched Fgure 3. Step 1.Get MIPS dle from each resource In the second step, a workflow enters the Grd System, and the scheduler realzes hs work, splts ths workflow accordng to the dle cycles of each resource, fg 4. Workflow 5 I_J T1 T2 T Fgure 4. Step2.Splt workflow accordng dle MIPS of each resource In the next step, fg. 5, each resource executes hs task, fnshng the executon n a smlar tme for all the resources, so we do not depend on the executon of the worst resource. T1 T2 T3 R1 R2 R3 Fgure 5. Step3. Execute the tasks n the correspondng resources In the last step, the scheduler obtans the results for ths teraton and the dle MIPS that they have for the executon of the next teraton. ISSN: Issue 1, Volume 8, January 29

5 A. J. Sanchez Santago, A. J. Yuste, J. E. Munoz Exposto, S. Garca Galan, J. M. Maquera Marn, S. Bruque 3348 MIPS R1 R2 R3 RESULTS(R1) 287 MIPS RESULTS(R2) Sched 3157 MIPS RESULTS(R3) Fgure 6. Step 4. Get results and the dle MIPS for the next teraton As seen durng the example, our scheduler s hghly dynamc, because after every executon, the scheduler s able to re-obtan the actual number of dle cycles. 4 Expermental results Due to the dffcultes assocated to real tests, the benefts of ths scheduler have been verfed through the use of smulatons. Smulatons offer bg benefts opposte to other methods that are currently used to measure a system s behavor: a. Smulatons allow expermentaton n the system wthout nterruptng the actvty of the system and therefore avodng potental damages. b. Smulatons are ndependent on the process or real system. Therefore, t s possble to use smulatons n dfferent stages before ther actual mplementaton n real settngs. c. Smulatons mprove the comprehenson of the behavour of the system snce they provde numercal results. d. Smulatons allow to carry out comparatve analyses of dfferent scenaros. Therefore, t was necessary to develop our scheduler usng the GrdSm toolkt [4]. GrdSm s a smulaton toolkt for resource modellng and applcaton schedulng n parallel and dstrbuted computng systems. Table 1 shows the type of the resources we have used n our analyss. The MIPS for each PE (Processng Element) s the maxmum MIPS for each resource. Ths value can change accordng to the use of CPU's cycles that the user of the resource needs at any gven moment, modelled by a unform dstrbuton. We assume that delay of transmsson s neglgble compared wth the tme of executon of a task. Resourc. R1 R2 R3 R4 R5 S.O. Wn Unx Wn Wn Wn Arq. IBM Solars IBM IBM IBM P.E MAX MIPS Table 1. Type of resources We compare our proposal wth other, more tradtonal schedulers, space-shared and tme-shared polces. (Frst-Come Frst-Served) [15] s a tme-shared algorthm. Ths s one of the smplest, executes the workflows to completon n the order they are submtted. It has a bg drawback; f a resource has few dle cycles to offer, the executon of the remanng workflows wll turn out slow, snce t s necessary to fulfl all tasks of a workflow to execute the followng workflow. (Round Robn) [16] s a tme-shared algorthm; t s one of the oldest, smplest, farest and most wdely used schedulng algorthms, desgned especally for tme-sharng systems. A small unt of tme, called tme slces or quantum s defned. All runable processes are kept n a crcular queue. The scheduler goes around ths queue, allocatng the CPU to each process for a tme nterval of one quantum. New processes are added to the tal of the queue. The scheduler pcks the frst process from the queue, sets a tmer to nterrupt after one quantum, and dspatches the process. If the process s stll runnng at the end of the quantum, the CPU s pre-empted and the process s added to the tal of the queue. If the process fnshes before the end of the quantum, the process tself releases the CPU voluntarly. In ether case, the CPU scheduler assgns the CPU to the next process n the ready queue. Every tme a process s granted the CPU, a context swtch occurs, whch adds overhead to the process executon tme. The experments can be dvded n two groups. Frstly, we have executed a workflow based on ndependent tasks that usually appear n optmzaton problems. Secondly, we executed a DAG problem. 4.1 An optmzaton problem The Genetc Algorthm (GA) s a method for solvng optmzaton problems that s based on natural selecton, the process that drves bologcal evoluton. The GA repeatedly modfes a populaton of ndvdual solutons. At each step, the GA randomly selects ndvduals from the current populaton to be parents and uses them to produce the chldren for the next generaton. Over successve generatons, the populaton evolves toward an optmal soluton. The GA can be appled to solve optmzaton problems that are not well suted for standard optmzaton algorthms, ncludng problems n whch the objectve functon s dscontnuous, non-dfferentable, stochastc, or hghly nonlnear as our problem. GA s frequently used n research lke n network communcatons [17-18], for speech/musc dscrmnaton [19] or for parttonng problems n code sgn [2]. We have carred out several smulatons by changng the number of teratons; each teraton beng made ISSN: Issue 1, Volume 8, January 29

6 A. J. Sanchez Santago, A. J. Yuste, J. E. Munoz Exposto, S. Garca Galan, J. M. Maquera Marn, S. Bruque up by a workflow and the avalable resources. We defned the length of a workflow large enough, to take advantage of a system grd, and moderately small n order to not overload the less effcent resources. We have developed four experments wth 25, 5, 75 and 1 resources that we explan n detal below Frst experment Tme(s) Resources The frst experment was made up by 25 resources and 1, 5 and 1 teratons. The tmes are shown n table 2, 3 and 4. We can see n fg. 7 that the executon tme was gettng shorter for the and balanced algorthms. Nº teratons=1 25 Algorthm Tme (sec.) , , ,56 Table 2. 1 teratons and 25 resources 2 Nº teratons=1 Nº teratons =5 Nº teratons=1 Fgure 7. Experment wth 25 resources and Workflow length= Second experment Table 5, 6 and 7, and fg. 8 show a comparson between the tme for the three schedulers for 5 resources and 1, 5 and 1 teratons. Fg. 8 depcts that the hgher the number of teratons, the larger the dfference of executon tme, whch mples that and degenerate very quckly as the number of teratons grows. Nº teratons =5 25 Algorthm Tme (sec.) , ,95 Table 3. 5 teratons and 25 resources Nº teratons=1 25 Algorthm Tme (sec.) , , ,76 Table 4. 1 teratons and 25 resources Nº teratons =1 5 Algorthm Tme (sec.) , , ,76 Table 5. 1 teratons and 5 resources Nº teratons =5 5 Algorthm Tme (sec.) , , ,16 Table 6. 5 teratons and 5 resources Nº teratons =1 5 Algorthm Tme (sec.) , , ,28 Table 7. 1 teratons and 5 resources ISSN: Issue 1, Volume 8, January 29

7 A. J. Sanchez Santago, A. J. Yuste, J. E. Munoz Exposto, S. Garca Galan, J. M. Maquera Marn, S. Bruque 75 Resources 5 Resources Tme(s) Tme(s) Nº teratons=1 Nº teratons =5 Nº teratons=1 Fgure 8. Experment wth 5 resources and Workflow length= Thrd experment Tables 8, 9 and 1, and fg. 9 exhbts tme of executon for 75 resources and 1, 5 and 1 teratons. Lke n the aforementoned experments, our balanced scheduler offers better results. Nº teratons =1 75 Algorthm Tme (sec.) 35268, , ,72 Table 8. 1 teratons and 75 resources Nº teratons =5 75 Algorthm Tme (sec.) 17669, , ,15 Table 9. 5 teratons and 75 resources Nº teratons =1 75 Algorthm Tme (sec.) , , ,64 Table 1. 1 teratons and 75 resources Nº teratons=1 Nº teratons =5 Nº teratons=1 Fgure 9. Experment wth 75 Resources and Fourth experment The last experment that we carred out, tables 11, 12, 13 and fg. 1, wth 1 resources, offer the mnmum executon tme of all our tests. Nº teratons=1 1 Algorthm Tme (sec.) 3551, , ,52 Table teratons and 1 resources Nº teratons =5 1 Algorthm Tme (sec.) , , ,72 Table teratons and 1 resources Nº teratons=1 Workflow lengh= 5 1 Algorthm Tme (sec.) , , ,2 Table teratons and 1 resources ISSN: Issue 1, Volume 8, January 29

8 A. J. Sanchez Santago, A. J. Yuste, J. E. Munoz Exposto, S. Garca Galan, J. M. Maquera Marn, S. Bruque 1 Resources 5 Iteratons Tme(s) Tme(s) Nº teratons=1 Nº teratons =5 Nº teratons= Fgure 1. Experment wth 1 Resources and Summary of results The prevous examples that we have carred out n ths study show a comparson of tmes from the pont of vew of the teratons, meanwhle ths last experment compares executon tmes accordng to the resources beng used. In fg. 11 we show a comparson for 1 teratons and 25, 5, 75 and 1 resources. The dynamcbalanced scheduler we proposed offers agan better results than any other two schedulers for every mx of resources Iteratons Tme(s) Fgure 12. Experment wth 5 teratons and Iteratons Fgure 13. Experment wth 1 teratons and Fg. 13 exhbts the executon tme for 1 teratons and 25, 5, 75 and 1 resources. Tm e(s) A DAG problem In ths experment we executed a standard DAG, whch conssted of a proten annotaton workflow (Fg. 14), [9]. Fgure 11. Experment wth 1 teratons and Fg. 12 exhbts the executon tme for 5 teratons and 25, 5, 75 and 1 resources. At any gven combnaton of resources our balanced scheduler obtans the shorter executon tme. ISSN: Issue 1, Volume 8, January 29

9 A. J. Sanchez Santago, A. J. Yuste, J. E. Munoz Exposto, S. Garca Galan, J. M. Maquera Marn, S. Bruque Fgure 14. Proten annotaton workflow Ths experment, proten annotaton workflow, was executed wth the parameters shown n Table 14. Resources 1 S.O. Wn Arq. IBM MAX MIPS 6 BaudRate(Mb) 1 Prop. Delay(ms) 1 Table 14. Parameters for Grd System for proten annotaton workflow Durng ths second experment, our dynamcbalanced scheduler mproves the tme of executon of the and schedulers. Results are presented n Table 15. Prot. annot. Tme (sec) workflow Table 15. Executon tme; proten annotaton workflow 5 Conclusons and future work A grd envronment s usually heterogeneous n nature n the real world at least for the dfferent computng speeds at dfferent partcpatng stes. The heterogenety presents a challenge for effectvely arrangng load sharng actvtes n a computatonal grd. Ths paper explores job schedulng and allocaton ssues n heterogeneous computatonal grds when a task, durng the schedulng process, cannot ft n any sngle ste n the grd. We have proposed a balanced scheduler n a grd envronment. Wth ths technque we have fulflled three objectves: Frstly executon tme has been shortened, secondly we have obtaned a better loadng dstrbuton among resources n the grd and thrdly we have used dle, non cost-ncreasng resources. As future research drectons, we wll conduct addtonal experments to comprehensvely compare other schedulng strateges usng fuzzy logc that would enable us to set up a utlty grd structure. These utlty models are very effectve n shared nter-organzatonal settngs to ncrease computng capacty whlst keepng securty and reducng operatng costs. 6 Acknowledgments Ths work has been fnancally supported by the Andalusan Government (Research Project P6-SEJ- 1694). References [1] I. Foster and C. Kesselman (edtors), The Grd: Blueprnt for a new Computng nfrastructure. Morgan Kaufmann Publshers, San Francsco, USA, (1999) [2] R. Buyya, D. Abramson, and J. Gddy, Nmrod/G: An Archtecture for a Resource Management and Schedulng System n a Global Computatonal Grd, 4th Internatonal Conference and Exhbton on Hgh Performance Computng n Asa-Pacfc Regon (HPC ASIA 2), Vol 1,2, pp 283. [3] Reakook Hwang, Mtsuo Gen, Hrosh Katayama. A comparson of multprocessor task schedulng algorthms wth communcaton costs. Computers and Operatons Research Volume 35, Issue 3, 28, pp [4] Anthony Sulsto, Uros Cbej, Srkumar Venugopal, Borut Robc and Rajkumar Buyya A Toolkt for Modellng and Smulatng Data Grds: An Extenson to GrdSm. Concurrency and Computaton: Practce and Experence (CCPE), Wley Press, New York, USA 27. ISSN: Issue 1, Volume 8, January 29

10 A. J. Sanchez Santago, A. J. Yuste, J. E. Munoz Exposto, S. Garca Galan, J. M. Maquera Marn, S. Bruque [5] Cao, J., Spooner, D.P., Jarvs, S.A., Nudd, G.R.: Grd load balancng usng ntellgent agents. Future Generaton Comput. Syst. Vol. 21(1), 25, pp [6] Mequn Lu, Kun Gao, Zhong Wan, A Novel Archtecture for Data Mnng Grd Scheduler, WSEAS Transactons on Systems Issue 1, Volume 7, 28 pp [7] Wang Da-Zhen, Zhan Jun-Shan, Wan Fang, Zhu Le., A Dynamc Task Schedulng Algorthm n Grd Envronment. WSEAS Transactons on Computers, Issue 7, Volume 5, 26, pp [8] Reche López, P., Gómez González, M., Ruz Reyes, N., Jurado F., Optmzaton of bomass fuelled systems for dstrbuted power generaton usng Partcle Swarm Optmzaton. Electrc Power Systems Research. Issue 8, Vol. 78, 28, pp Knowledge Based Systems (IPMU), Torremolnos(Span) pp , 28. [19] J.E. Muñoz, S. García Galán, N. Ruz-Reyes, P. Vera- Candeas, Audo codng mprovement usng evolutonary speech/musc dscrmnaton. IEEE Internatonal Conference on Fuzzy Systems (FUZZY-IEEE 27), London, pp 1-6, 27. [2] Abderrazak Henn, Mouloud Koudl, Karma Benatchba, Hassane Oumsalem, Kamel Chaouche, A parallel envronment usng taboo search and genetc algorthms for solvng parttonng problems n codesgn, WSEAS Transactons on Systems, Issue 1, Volume 3, 24, pp [9] Yu, J. and Buyya, R., A Budget Constraned Schedulng of Workflow Applcatons on Utlty Grds usng Genetc Algorthms, Workshop on Workflows n Support of Large-Scale Scence, Proceedngs of the 15th IEEE Internatonal Symposum on Hgh Performance Dstrbuted Computng (HPDC 26, IEEE CS Press, Los Alamtos, CA, USA), 26, Pars, France. [1] Duan, R., Prodan, R., Fahrnger, T.: Run-tme optmsaton of grd workflow applcatons, 7 th IEE/ACM Internatonal Conference on Grd Computng, 26, pp [11] Spooner, D.P., Cao, J., Jarvs, S.A., He, L., Nudd, G.R.: Performance-Aware Workflow Management for Grd Computng. The Computer Journal, Vol 48, 25, pp [12] Mandal, A., Kennedy, K., Koelbel, C., Marn, G., Mellor- Crummey, J., Lu, B., Johnsson, L.: Schedulng strateges for mappng applcaton workflows onto the grd. Proceedngs of the 14th Internatonal Symposum on Hgh Performance Dstrbuted Computng (HPDC 25), 25, pp [13] Blythe, J., Jan, S., Deelman, E., Gl, Y., Vah, K., Mandal, A., Kennedy, K.: Task schedulng strateges for workflow-based applcatons n grds. Proceedngs of the Cluster Computng and Grd 25 (CCGrd 25), 25, pp [14] Wess, G., Pnedo, M.: Schedulng Tasks wth Exponental Servce Tmes on Non- Identcal Processors to Mnmze Varous Cost Functons. Journal of Appled Probablty, 198, pp [15] Domenco Ferrar. Real-tme communcaton n an nternetwork. Journal of Hgh Speed Networks, Vol. 1, [16] Tanebaum, A.S., Modern operatng systems. Prentce-Hall, [17] Antono J. Yuste, Francsco Davd Trujllo, Alca Trvño, Eduardo Caslar, An adaptve gateway dscovery for moble ad hoc networks. Proceedngs of the 5th ACM nternatonal workshop on Moblty management and wreless access, MobWac '7, 27, pp [18] J. A. Fernández Preto, Juan R. Velasco Pérez. Adaptve Genetc Algorthm control parameter optmzaton to verfy the network protocol performance. 12th Internatonal Conference on Informaton Processng and Management of Uncertanty n ISSN: Issue 1, Volume 8, January 29