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Conference Paper: Cost-minimizing preemptive scheduling of mapreduce workloads on hybrid clouds

TitleCost-minimizing preemptive scheduling of mapreduce workloads on hybrid clouds
Authors
KeywordsCloud platforms
Efficient scheduling
Hybrid clouds
On-line algorithms
Pre-emptive scheduling
Private clouds
Programming models
Task completion time
Issue Date2013
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000600
Citation
The IEEE/ACM 21st International Symposium on Quality of Service (IWQoS 2013), Montreal, QC., 3-4 June 2013. In International Workshop on Quality of Service, 2013, p. 213-218 How to Cite?
AbstractMapReduce has become the dominant programming model for processing massive amounts of data on cloud platforms. More and more enterprises are now utilizing hybrid clouds, consisting of private infrastructure owned by themselves and public clouds such as Amazon EC2, to process their spiky MapReduce workloads, which fully utilize their own on-premise resources while outsourcing the tasks only when needed. With disparate workloads of different MapReduce tasks, an efficient scheduling mechanism is in need to enable efficient utilization of the on-premise resources and to minimize the task outsourcing cost, while meeting the task completion time requirements as well. In this paper, a fine-grained model is described to characterize the scheduling of heterogeneous MapReduce workloads, and an online algorithm is proposed for joint task admission control into the private cloud, task outsourcing to the public cloud, and VM allocation to execute the admitted tasks on the private cloud, such that the time-averaged task outsourcing cost is minimized over the long run. The online algorithm features preemptive scheduling of the tasks, where a task executed partially on the on-premise infrastructure can be paused and scheduled to run later. It also achieves desirable properties such as meeting a pre-set task admission ratio and bounding the worst-case task completion time, as proven by our rigorous theoretical analysis. © 2013 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/186483
ISBN
ISSN

 

DC FieldValueLanguage
dc.contributor.authorQiu, Xen_US
dc.contributor.authorYeow, WLen_US
dc.contributor.authorWu, Cen_US
dc.contributor.authorLau, FCMen_US
dc.date.accessioned2013-08-20T12:11:10Z-
dc.date.available2013-08-20T12:11:10Z-
dc.date.issued2013en_US
dc.identifier.citationThe IEEE/ACM 21st International Symposium on Quality of Service (IWQoS 2013), Montreal, QC., 3-4 June 2013. In International Workshop on Quality of Service, 2013, p. 213-218en_US
dc.identifier.isbn978-1-4799-0590-4-
dc.identifier.issn1548-615X-
dc.identifier.urihttp://hdl.handle.net/10722/186483-
dc.description.abstractMapReduce has become the dominant programming model for processing massive amounts of data on cloud platforms. More and more enterprises are now utilizing hybrid clouds, consisting of private infrastructure owned by themselves and public clouds such as Amazon EC2, to process their spiky MapReduce workloads, which fully utilize their own on-premise resources while outsourcing the tasks only when needed. With disparate workloads of different MapReduce tasks, an efficient scheduling mechanism is in need to enable efficient utilization of the on-premise resources and to minimize the task outsourcing cost, while meeting the task completion time requirements as well. In this paper, a fine-grained model is described to characterize the scheduling of heterogeneous MapReduce workloads, and an online algorithm is proposed for joint task admission control into the private cloud, task outsourcing to the public cloud, and VM allocation to execute the admitted tasks on the private cloud, such that the time-averaged task outsourcing cost is minimized over the long run. The online algorithm features preemptive scheduling of the tasks, where a task executed partially on the on-premise infrastructure can be paused and scheduled to run later. It also achieves desirable properties such as meeting a pre-set task admission ratio and bounding the worst-case task completion time, as proven by our rigorous theoretical analysis. © 2013 IEEE.-
dc.languageengen_US
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000600-
dc.relation.ispartofInternational Workshop on Quality of Serviceen_US
dc.rightsInternational Workshop on Quality of Service. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©2013 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectCloud platforms-
dc.subjectEfficient scheduling-
dc.subjectHybrid clouds-
dc.subjectOn-line algorithms-
dc.subjectPre-emptive scheduling-
dc.subjectPrivate clouds-
dc.subjectProgramming models-
dc.subjectTask completion time-
dc.titleCost-minimizing preemptive scheduling of mapreduce workloads on hybrid cloudsen_US
dc.typeConference_Paperen_US
dc.identifier.emailQiu, X: xjqiu@cs.hku.hken_US
dc.identifier.emailWu, C: cwu@cs.hku.hken_US
dc.identifier.emailLau, FCM: fcmlau@cs.hku.hk-
dc.identifier.authorityWu, C=rp01397en_US
dc.identifier.authorityLau, FCM=rp00221en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/IWQoS.2013.6550284-
dc.identifier.scopuseid_2-s2.0-84881354193-
dc.identifier.hkuros217647en_US
dc.identifier.spage213-
dc.identifier.epage218-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 140116-

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