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Task matching and scheduling in heterogeneous computing environments using a genetic-algorithm-based approach

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dc.contributor.authorMaciejewski, Anthony A., author
dc.contributor.authorRoychowdhury, Vwani P., author
dc.contributor.authorSiegel, Howard Jay, author
dc.contributor.authorWang, Lee, author
dc.contributor.authorAcademic Press, publisher
dc.date.accessioned2007-01-03T08:09:32Z
dc.date.available2007-01-03T08:09:32Z
dc.date.issued1997
dc.description.abstractTo exploit a heterogeneous computing (HC) environment, an application task may be decomposed into subtasks that have data dependencies. Subtask matching and scheduling consists of assigning subtasks to machines, ordering subtask execution for each machine, and ordering intermachine data transfers. The goal is to achieve the minimal completion time for the task. A heuristic approach based on a genetic algorithm is developed to do matching and scheduling in HC environments. It is assumed that the matcher/scheduler is in control of a dedicated HC suite of machines. The characteristics of this genetic-algorithm-based approach include: separation of the matching and the scheduling representations, independence of the chromosome structure from the details of the communication subsystem, and consideration of overlap among all computations and communications that obey subtask precedence constraints. It is applicable to the static scheduling of production jobs and can be readily used to collectively schedule a set of tasks that are decomposed into subtasks. Some parameters and the selection scheme of the genetic algorithm were chosen experimentally to achieve the best performance. Extensive simulation tests were conducted. For small-sized problems (e.g., a small number of subtasks and a small number of machines), exhaustive searches were used to verify that this genetic-algorithm-based approach found the optimal solutions. Simulation results for larger-sized problems showed that this genetic-algorithm-based approach outperformed two nonevolutionary heuristics and a random search.
dc.format.mediumborn digital
dc.format.mediumarticles
dc.identifier.bibliographicCitationWang, Lee, et al., Task Matching and Scheduling in Heterogeneous Computing Environments Using a Genetic-Algorithm-Based Approach, Journal of Parallel and Distributed Computing 47: no. 1 (November 25, 1997): 8-22.
dc.identifier.urihttp://hdl.handle.net/10217/67357
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofFaculty Publications
dc.rights©1997 Academic Press.
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectglobal data item
dc.subjectopen research problems
dc.subjectapplication task
dc.subjectgenetic algorithms
dc.subjectchromosome representation
dc.titleTask matching and scheduling in heterogeneous computing environments using a genetic-algorithm-based approach
dc.typeText

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