Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61154
PIRA download icon_1.1View/Download Full Text
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorLing, X-
dc.creatorYuan, Y-
dc.creatorWang, D-
dc.creatorLiu, J-
dc.creatorYang, J-
dc.date.accessioned2016-12-19T08:54:58Z-
dc.date.available2016-12-19T08:54:58Z-
dc.identifier.issn0743-7315en_US
dc.identifier.urihttp://hdl.handle.net/10397/61154-
dc.language.isoenen_US
dc.publisherAcademic Pressen_US
dc.rights© 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en_US
dc.rightsThe following publication Ling, X., Yuan, Y., Wang, D., Liu, J., & Yang, J. (2016). Joint scheduling of mapreduce jobs with servers: Performance bounds and experiments. Journal of Parallel and Distributed Computing, 90, 52-66 is available at https://doi.org/10.1016/j.jpdc.2016.02.002en_US
dc.subjectFast heuristicen_US
dc.subjectMapReduceen_US
dc.subjectNP-completeen_US
dc.subjectSchedulingen_US
dc.subjectServer assignmenten_US
dc.titleJoint scheduling of MapReduce jobs with servers : performance bounds and experimentsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage52en_US
dc.identifier.epage66en_US
dc.identifier.volume90-91en_US
dc.identifier.doi10.1016/j.jpdc.2016.02.002en_US
dcterms.abstractMapReduce-like frameworks have achieved tremendous success for large-scale data processing in data centers. A key feature distinguishing MapReduce from previous parallel models is that it interleaves parallel and sequential computation. Past schemes, and especially their theoretical bounds, on general parallel models are therefore, unlikely to be applied to MapReduce directly. There are many recent studies on MapReduce job and task scheduling. These studies assume that the servers are assigned in advance. In current data centers, multiple MapReduce jobs of different importance levels run together. In this paper, we investigate a schedule problem for MapReduce taking server assignment into consideration as well. We formulate a MapReduce server-job organizer problem (MSJO) and show that it is NP-complete. We develop a 3-approximation algorithm and a fast heuristic design. Moreover, we further propose a novel fine-grained practical algorithm for general MapReduce-like task scheduling problem. Finally, we evaluate our algorithms through both simulations and experiments on Amazon EC2 with an implementation with Hadoop. The results confirm the superiority of our algorithms.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationJournal of parallel and distributed computing, 2016, v. 90-91, p. 52-66-
dcterms.isPartOfJournal of parallel and distributed computing-
dcterms.issued2016-
dc.identifier.isiWOS:000374627000005-
dc.identifier.scopus2-s2.0-84961784554-
dc.description.validate201901_a bcmaen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
Appears in Collections:Journal/Magazine Article
Files in This Item:
File Description SizeFormat 
Ling_Joint_scheduling_MapReduce.pdf1.19 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show simple item record

Page views

151
Last Week
1
Last month
Citations as of Jul 14, 2024

Downloads

173
Citations as of Jul 14, 2024

SCOPUSTM   
Citations

18
Last Week
0
Last month
Citations as of Jul 19, 2024

WEB OF SCIENCETM
Citations

9
Last Week
0
Last month
Citations as of Jul 18, 2024

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.