Dynamic Mapping of a Class of Independent Tasks onto Heterogeneous Computing Systems

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dc.contributor.authorMaheswaran, Muthucumaru
dc.contributor.authorAli, Shoukat
dc.contributor.authorSiegel, Howard Jay
dc.contributor.authorHensgen, Debra
dc.contributor.authorFreund, Richard F.
dc.contributor.corporateCenter for Information Systems Security Studies and Research (CISR)
dc.dateJune 1999
dc.date.accessioned2013-08-20T21:32:49Z
dc.date.available2013-08-20T21:32:49Z
dc.date.issued1999-06
dc.description.abstractDynamic mapping (matching and scheduling) heuristics for a class of independent tasks using heterogeneous distributed computing systems are studied. Two types of mapping heuristics are considered: im- mediate mode and batch mode heuristics. Three new heuristics, one for batch mode and two for immediate mode, are introduced as part of this research. Simulation studies are performed to compare these heuristics with some existing ones. In total, five immediate mode heuristics and three batch mode heuristics are ex- amined. The immediate mode dynamic heuristics consider, to varying degrees and in different ways, task affinity for different machines and machine ready times. The batch mode dynamic heuristics consider these factors, as well as aging of tasks waiting to execute. The simulation results reveal that the choice of which dynamic mapping heuristic to use in a given heterogeneous environment depends on parameters such as: (a) the structure of the heterogeneity among tasks and machines, and (b) the arrival rate of the tasks.en_US
dc.identifier.urihttps://hdl.handle.net/10945/35384
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.subject.authorNAen_US
dc.titleDynamic Mapping of a Class of Independent Tasks onto Heterogeneous Computing Systemsen_US
dspace.entity.typePublication
relation.isOrgUnitOfPublicationc1079eb2-3f30-4129-a9f8-c5ae0836cc40
relation.isOrgUnitOfPublication.latestForDiscoveryc1079eb2-3f30-4129-a9f8-c5ae0836cc40
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