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Dynamically mapping tasks with priorities and multiple deadlines in a heterogeneous environment

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dc.contributor.authorYellampalli, Siva Sankar, author
dc.contributor.authorVangari, Praveen, author
dc.contributor.authorSripada, Siddhartha, author
dc.contributor.authorSharma, Ashish, author
dc.contributor.authorKaul, Aditya, author
dc.contributor.authorJoshi, Rohit, author
dc.contributor.authorDilmaghani, Raheleh B., author
dc.contributor.authorChitta, Ramakrishna, author
dc.contributor.authorTideman, Sonja, author
dc.contributor.authorSchneider, Myron, author
dc.contributor.authorBraun, Tracy D., author
dc.contributor.authorMaciejewski, Anthony A., author
dc.contributor.authorSiegel, Howard Jay, author
dc.contributor.authorShivle, Sameer, author
dc.contributor.authorKim, Jong-Kook, author
dc.contributor.authorElsevier Inc., publisher
dc.date.accessioned2007-01-03T08:09:35Z
dc.date.available2007-01-03T08:09:35Z
dc.date.issued2006
dc.description.abstractIn a distributed heterogeneous computing system, the resources have different capabilities and tasks have different requirements. To maximize the performance of the system, it is essential to assign the resources to tasks (match) and order the execution of tasks on each resource (schedule) to exploit the heterogeneity of the resources and tasks. Dynamic mapping (defined as matching and scheduling) is performed when the arrival of tasks is not known a priori. In the heterogeneous environment considered in this study, tasks arrive randomly, tasks are independent (i.e., no inter-task communication), and tasks have priorities and multiple soft deadlines. The value of a task is calculated based on the priority of the task and the completion time of the task with respect to its deadlines. The goal of a dynamic mapping heuristic in this research is to maximize the value accrued of completed tasks in a given interval of time. This research proposes, evaluates, and compares eight dynamic mapping heuristics. Two static mapping schemes (all arrival information of tasks are known) are designed also for comparison. The performance of the best heuristics is 84% of a calculated upper bound for the scenarios considered.
dc.format.mediumborn digital
dc.format.mediumarticles
dc.identifier.bibliographicCitationKim, Jong-Kook, et al., Dynamically Mapping Tasks with Priorities and Multiple Deadlines in a Heterogeneous Environment, Journal of Parallel and Distributed Computing 67, no. 2 (February 2007): [154]-169.
dc.identifier.urihttp://hdl.handle.net/10217/67373
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofFaculty Publications
dc.rights©2006 Elsevier 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.subjectresource allocation
dc.subjectdistributed computing
dc.subjectdynamic mapping
dc.subjectresource management
dc.subjectscheduling
dc.subjectheterogeneous computing
dc.subjectstatic mapping
dc.subjectpriority
dc.subjectdeadlines
dc.titleDynamically mapping tasks with priorities and multiple deadlines in a heterogeneous environment
dc.typeText

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