Improving the performance of independent task assignment heuristics Minmin, Maxmin and Sufferage

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buir.contributor.authorAykanat, Cevdet
dc.citation.epage1256en_US
dc.citation.issueNumber5en_US
dc.citation.spage1244en_US
dc.citation.volumeNumber25en_US
dc.contributor.authorTabak, E. K.en_US
dc.contributor.authorCambazoglu, B. B.en_US
dc.contributor.authorAykanat, Cevdeten_US
dc.date.accessioned2016-02-08T11:00:22Z
dc.date.available2016-02-08T11:00:22Z
dc.date.issued2014en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractMinMin, MaxMin, and Sufferage are constructive heuristics that are widely and successfully used in assigning independent tasks to processors in heterogeneous computing systems. All three heuristics are known to run in O(K N2) time in assigning N tasks to K processors. In this paper, we propose an algorithmic improvement that asymptotically decreases the running time complexity of MinMin to O(K N log N) without affecting its solution quality. Furthermore, we combine the newly proposed MinMin algorithm with MaxMin as well as Sufferage, obtaining two hybrid algorithms. The motivation behind the former hybrid algorithm is to address the drawback of MaxMin in solving problem instances with highly skewed cost distributions while also improving the running time performance of MaxMin. The latter hybrid algorithm improves the running time performance of Sufferage without degrading its solution quality. The proposed algorithms are easy to implement and we illustrate them through detailed pseudocodes. The experimental results over a large number of real-life data sets show that the proposed fast MinMin algorithm and the proposed hybrid algorithms perform significantly better than their traditional counterparts as well as more recent state-of-the-art assignment heuristics. For the large data sets used in the experiments, MinMin, MaxMin, and Sufferage, as well as recent state-of-the-art heuristics, require days, weeks, or even months to produce a solution, whereas all of the proposed algorithms produce solutions within only two or three minutes. © 2013 IEEE.en_US
dc.identifier.doi10.1109/TPDS.2013.107en_US
dc.identifier.issn1045-9219
dc.identifier.urihttp://hdl.handle.net/11693/26477
dc.language.isoEnglishen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/TPDS.2013.107en_US
dc.source.titleIEEE Transactions on Parallel and Distributed Systemsen_US
dc.subjectConstructive heuristicsen_US
dc.subjectParallel processorsen_US
dc.subjectHeterogeneous systemsen_US
dc.subjectIndependent task assignmenten_US
dc.subjectLoad balancingen_US
dc.subjectMaxMinen_US
dc.subjectMinMinen_US
dc.subjectSufferageen_US
dc.titleImproving the performance of independent task assignment heuristics Minmin, Maxmin and Sufferageen_US
dc.typeArticleen_US

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