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dc.contributor.authorPadmanabhan, Meghana
dc.contributor.authorLi, Heyse
dc.contributor.authorTran, Tony T.
dc.contributor.authorDown, Douglas G.
dc.contributor.authorBeck, J. Christopher
dc.contributor.authorZhang, Yun
dc.date.accessioned2018-06-27T14:27:35Z
dc.date.available2018-06-27T14:27:35Z
dc.date.issued2017-07
dc.identifier.issn1094-6136
dc.identifier.issn1099-1425
dc.identifier.urihttp://hdl.handle.net/1721.1/116657
dc.description.abstractThis paper presents a three-stage algorithm for resource-aware scheduling of computational jobs in a large-scale heterogeneous data center. The algorithm aims to allocate job classes to machine configurations to attain an efficient mapping between job resource request profiles and machine resource capacity profiles. The first stage uses a queueing model that treats the system in an aggregated manner with pooled machines and jobs represented as a fluid flow. The latter two stages use combinatorial optimization techniques to solve a shorter-term, more accurate representation of the problem using the first-stage, long-term solution for heuristic guidance. In the second stage, jobs and machines are discretized. A linear programming model is used to obtain a solution to the discrete problem that maximizes the system capacity given a restriction on the job class and machine configuration pairings based on the solution of the first stage. The final stage is a scheduling policy that uses the solution from the second stage to guide the dispatching of arriving jobs to machines. We present experimental results of our algorithm on both Google workload trace data and generated data and show that it outperforms existing schedulers. These results illustrate the importance of considering heterogeneity of both job and machine configuration profiles in making effective scheduling decisions. Keywords: Resource-aware scheduling, Dynamic scheduling, Heterogeneous serversen_US
dc.description.sponsorshipGoogle (Firm) (Research Award)en_US
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canadaen_US
dc.publisherSpringer USen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s10951-017-0537-xen_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceSpringer USen_US
dc.titleMulti-stage resource-aware scheduling for data centers with heterogeneous serversen_US
dc.typeArticleen_US
dc.identifier.citationTran, Tony T., et al. “Multi-Stage Resource-Aware Scheduling for Data Centers with Heterogeneous Servers.” Journal of Scheduling, vol. 21, no. 2, Apr. 2018, pp. 251–67.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Data, Systems, and Societyen_US
dc.contributor.mitauthorZhang, Yun
dc.relation.journalJournal of Schedulingen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2018-03-07T05:22:52Z
dc.language.rfc3066en
dc.rights.holderSpringer Science+Business Media, LLC
dspace.orderedauthorsTran, Tony T.; Padmanabhan, Meghana; Zhang, Peter Yun; Li, Heyse; Down, Douglas G.; Beck, J. Christopheren_US
dspace.embargo.termsNen
dc.identifier.orcidhttps://orcid.org/0000-0002-0422-834X
mit.licensePUBLISHER_POLICYen_US


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