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Resource allocation for heterogeneous computing systems: performance criteria, robustness measures, optimization heuristics, and properties

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dc.contributor.authorBriceno Guerrero, Luis Diego, author
dc.contributor.authorSiegel, Howard Jay, advisor
dc.contributor.authorMaciejewski, Anthony A., advisor
dc.contributor.authorBöhm, Anton Pedro Willem, 1948-, committee member
dc.contributor.authorJayasumana, Anura P., committee member
dc.contributor.authorSmith, James T., committee member
dc.date.accessioned2007-01-03T04:41:35Z
dc.date.available2007-01-03T04:41:35Z
dc.date.issued2010
dc.descriptionDepartment Head: Anthony A. Maciejewski.
dc.description.abstractHeterogeneous computing (HC) is the coordinated use of different types of machines, networks, and interfaces to maximize the combined performance and/or cost effectiveness of the system. The application environments studied in this research are: a weather data processing system, a massive multi-player on-line gaming system, and a distributed satellite image processing system. Each one of these application environments was simulated on different computation platforms. Contributions for each environment: (1) mathematical model of environment, (2) defined a performance criterion, (3) defined robustness metric, (4) designed resource allocation heuristics based on performance and robustness measures, and (5) conducted simulation studies for evaluating and comparing heuristic techniques. We consider an iterative approach that decreases the finishing time of machines by repeatedly executing a resource allocation heuristic to minimize the make span of the considered machines and tasks. For each successive iteration, the make span machine of the previous iteration and the tasks assigned to it are removed from the set of considered machines and tasks. The contribution include identifying which characteristics heuristics need to generate improvement with the iterative approach, showing that the effectiveness of the iterative approach is heuristic dependent, and deriving a theorem to identify which heuristics cannot attain improvements.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierBricenoGuerrero_colostate_0053A_10080.pdf
dc.identifierETDF2010100006ECEN
dc.identifier.urihttp://hdl.handle.net/10217/40277
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
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.subjectComputing algorithms
dc.subjectheterogeneous computing
dc.subjectrobustness
dc.subjectresource allocation
dc.subjectheuristics
dc.subject.lcshHeterogeneous computing
dc.subject.lcshParallel processing (Electronic computers)
dc.subject.lcshHeuristic programming
dc.titleResource allocation for heterogeneous computing systems: performance criteria, robustness measures, optimization heuristics, and properties
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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