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Mapping tasks onto distributed heterogeneous computing systems using a genetic algorithm approach

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dc.contributor.authorSiegel, Howard Jay, author
dc.contributor.authorBraun, Tracy D., author
dc.contributor.authorTheys, Mitchell D., author
dc.contributor.authorMaciejewski, Anthony A., author
dc.contributor.authorJohn Wiley & Sons, Inc., publisher
dc.date.accessioned2007-01-03T08:09:39Z
dc.date.available2007-01-03T08:09:39Z
dc.date.issued2001
dc.description.abstractMuch work has been done using GAs of various types to solve the problem of matching and scheduling of tasks and meta-tasks in a mixed-machine distributed, heterogeneous computing environment. This chapter has discussed three types of genetic algorithms that have been studied to solve this problem. Each implementation has particular specifications and qualifications that were being met. In all cases, the genetic algorithm proved to be a useful method for solving the making and scheduling problem being researched.
dc.format.mediumborn digital
dc.format.mediumchapter titles
dc.identifier.bibliographicCitationTheys, Mitchell D., et al., Mapping Tasks onto Distributed Heterogeneous Computing Systems Using a Genetic Alogrithm Approach, Zomaya, Albert Y., Fikret Ercal, and Stephan Olariu, eds. Solutions to Parallel and Distributed Computing Problems: Lessons from Biological Sciences, 135-178. New York: John Wiley & Sons, Inc, 2001.
dc.identifier.urihttp://hdl.handle.net/10217/67397
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofFaculty Publications
dc.rights©2001 John Wiley & Sons, Inc.
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.titleMapping tasks onto distributed heterogeneous computing systems using a genetic algorithm approach
dc.typeText

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