Characterizing resource allocation heuristics for heterogeneous computing systems

dc.contributor.authorYao, Bin, author
dc.contributor.authorTheys, Mitchell D., author
dc.contributor.authorRobertson, James P., author
dc.contributor.authorReuther, Albert I., author
dc.contributor.authorMaheswaran, Muthucumaru, author
dc.contributor.authorBölöni, Ladislau, author
dc.contributor.authorBeck Noah, author
dc.contributor.authorMaciejewski, Anthony A., author
dc.contributor.authorSiegel, Howard Jay, author
dc.contributor.authorBraun, Tracy D., author
dc.contributor.authorAli, Shoukat, author
dc.contributor.authorElsevier Inc., publisher
dc.description.abstractIn many distributed computing environments, collections of applications need to be processed using a set of heterogeneous computing (HC) resources to maximize some performance goal. An important research problem in these environments is how to assign resources to applications (matching) and order the execution of the applications (scheduling) so as to maximize some performance criterion without violating any constraints. This process of matching and scheduling is called mapping. To make meaningful comparisons among mapping heuristics, a system designer needs to understand the assumptions made by the heuristics for (1) the model used for the application and communication tasks, (2) the model used for system platforms, and (3) the attributes of the mapping heuristics. This chapter presents a three-part classification scheme (3PCS) for HC systems. The 3PCS is useful for researchers who want to (a) understand a mapper given in the literature, (b) describe their design of a mapper more thoroughly by using a common standard, and (c) select a mapper to match a given real-world environment.
dc.format.mediumborn digital
dc.format.mediumproceedings (reports)
dc.identifier.bibliographicCitationAli, Shoukat, et al., Characterizing Resource Allocation Heuristics for Heterogeneous Computing Systems, Hurson, Ali R., ed., Advances in Computers. Volume 63, Parallel, Distributed, and Pervasive Computing, Amsterdam: Elsevier Inc., 2005: 38 p.
dc.publisherColorado State University. Libraries
dc.relation.ispartofFaculty Publications
dc.rights©2005 Elsevier Inc.
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dc.titleCharacterizing resource allocation heuristics for heterogeneous computing systems
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