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Article: Adaptive Algorithm for Minimizing Cloud Task Length with Prediction Errors

TitleAdaptive Algorithm for Minimizing Cloud Task Length with Prediction Errors
Authors
Issue Date2014
PublisherIEEE.
Citation
IEEE Transactions on Cloud Computing, 2014, v. 2, p. 194 - 207 How to Cite?
AbstractCompared to traditional distributed computing like grid system, it is non-trivial to optimize cloud task's execution performance due to its more constraints like user payment budget and divisible resource demand. In this paper, we analyze in-depth our proposed optimal algorithm minimizing task execution length with divisible resources and payment budget: 1) We derive the upper bound of cloud task length, by taking into account both workload prediction errors and hostload prediction errors. With such state-of-the-art bounds, the worst-case task execution performance is predictable, which can improve the quality of service in turn. 2) We design a dynamic version for the algorithm to adapt to the load dynamics over task execution progress, further improving the resource utilization. 3) We rigorously build a cloud prototype over a real cluster environment with 56 virtual machines, and evaluate our algorithm with different levels of resource contention. Cloud users in our cloud system are able to compose various tasks based on off-the-shelf web services. Experiments show that task execution lengths under our algorithm are always close to their theoretical optimal values, even in a competitive situation with limited available resources. We also observe a high level of fair treatment on the resource allocation among all tasks.
Persistent Identifierhttp://hdl.handle.net/10722/204723

 

DC FieldValueLanguage
dc.contributor.authorDi, Sen_US
dc.contributor.authorWang, CLen_US
dc.contributor.authorCappello, Fen_US
dc.date.accessioned2014-09-20T00:31:55Z-
dc.date.available2014-09-20T00:31:55Z-
dc.date.issued2014en_US
dc.identifier.citationIEEE Transactions on Cloud Computing, 2014, v. 2, p. 194 - 207en_US
dc.identifier.urihttp://hdl.handle.net/10722/204723-
dc.description.abstractCompared to traditional distributed computing like grid system, it is non-trivial to optimize cloud task's execution performance due to its more constraints like user payment budget and divisible resource demand. In this paper, we analyze in-depth our proposed optimal algorithm minimizing task execution length with divisible resources and payment budget: 1) We derive the upper bound of cloud task length, by taking into account both workload prediction errors and hostload prediction errors. With such state-of-the-art bounds, the worst-case task execution performance is predictable, which can improve the quality of service in turn. 2) We design a dynamic version for the algorithm to adapt to the load dynamics over task execution progress, further improving the resource utilization. 3) We rigorously build a cloud prototype over a real cluster environment with 56 virtual machines, and evaluate our algorithm with different levels of resource contention. Cloud users in our cloud system are able to compose various tasks based on off-the-shelf web services. Experiments show that task execution lengths under our algorithm are always close to their theoretical optimal values, even in a competitive situation with limited available resources. We also observe a high level of fair treatment on the resource allocation among all tasks.en_US
dc.languageengen_US
dc.publisherIEEE.en_US
dc.relation.ispartofIEEE Transactions on Cloud Computingen_US
dc.rightsIEEE Transactions on Cloud Computing. Copyright © IEEE.en_US
dc.rights©20xx 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.en_US
dc.titleAdaptive Algorithm for Minimizing Cloud Task Length with Prediction Errorsen_US
dc.typeArticleen_US
dc.identifier.emailWang, CL: clwang@cs.hku.hken_US
dc.identifier.authorityWang, CL=rp00183en_US
dc.identifier.doi10.1109/TCC.2013.16en_US
dc.identifier.hkuros239285en_US
dc.identifier.volume2en_US
dc.identifier.spage194en_US
dc.identifier.epage207en_US

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