Job Scheduling Strategies for Parallel Processing

. 1 AchimStreit PreemptionBasedBack?ll. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 QuinnO. Snell,MarkJ. Clement,andDavidB. Jackson JobSchedulingfortheBlueGene/LSystem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 ElieKrevat,Jos´eG. Castan˜os,andJos´eE. Moreira SelectiveReservationStrategiesforBack?llJobScheduling. . . . . . . . . . . . . . . . . 55 SrividyaSrinivasan,RajkumarKettimuthu,VijaySubramani, andPonnuswamySadayappan Multiple-QueueBack?llingSchedulingwithPrioritiesandReservations forParallelSystems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 BarryG. LawsonandEvgeniaSmirni SchedulingJobsonParallelSystemsUsingaRelaxedBack?llStrategy. . . . . 88 WilliamA. Ward,Jr. ,CarrieL. Mahood,andJohnE. West TheImpactofMoreAccurateRequestedRuntimes onProductionJobSchedulingPerformance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Su-HuiChiang,AndreaArpaci-Dusseau,andMaryK. Vernon EconomicSchedulinginGridComputing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 CarstenErnemann,VolkerHamscher,andRaminYahyapour SNAP:AProtocolforNegotiatingServiceLevelAgreements andCoordinatingResourceManagementinDistributedSystems. . . . . . . . . . 153 KarlCzajkowski,IanFoster,CarlKesselman,VolkerSander, andStevenTuecke LocalversusGlobalSchedulerswithProcessorCo-allocation inMulticlusterSystems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184 AncaI. D. BucurandDickH. J. Epema PracticalHeterogeneousPlaceholderScheduling inOverlayMetacomputers:EarlyExperiences. . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 ChristopherPinchak,PaulLu,andMarkGoldenberg CurrentActivitiesintheSchedulingandResourceManagementArea oftheGlobalGridForum. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 BillNitzbergandJenniferM. Schopf Author Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 ASelf-TuningJobSchedulerFamily withDynamic PolicySwitching Achim Streit 2 PC - Paderborn Center for Parallel Computing, Paderborn University 33102 Paderborn, Germany streit@upb. de http://www. upb. de/pc2 Abstract. The performance of job scheduling policies strongly depends on the properties of the incoming jobs. If the job characteristics often change, the scheduling policy should follow these changes. For this p- pose the dynP job scheduler family has been developed. The idea is to dynamically switch the scheduling policy during runtime. In a basic v- sion the policy switching is controlled by two parameters. The basic concept of theself-tuning dynP scheduler is to compute virtual schedules for each policy in every scheduling step. That policy is chosen which generates the ’best’ schedule. The performance of the self-tuning dynP scheduler no longer depends on a adequate setting of the input parameters.