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dc.contributor.authorChen, Huikai
dc.contributor.authorWang, Frank
dc.contributor.authorHelian, Na
dc.date.accessioned2018-01-30T22:25:57Z
dc.date.available2018-01-30T22:25:57Z
dc.date.issued2018-01-23
dc.identifier.citationChen , H , Wang , F & Helian , N 2018 , ' Entropy4Cloud: Using Entropy-Based Complexity To Optimize Cloud Service Resource Management Computational Intelligence for Cloud Computing ' , IEEE Transactions on Emerging Topics in Computational Intelligence , vol. 2 , no. 1 , pp. 13-24 . https://doi.org/10.1109/TETCI.2017.2755691
dc.identifier.otherPURE: 12067325
dc.identifier.otherPURE UUID: 8ba65fc8-7df7-45bf-b9a4-64cd7b2a031f
dc.identifier.otherORCID: /0000-0001-6687-0306/work/64003366
dc.identifier.otherScopus: 85064148002
dc.identifier.urihttp://hdl.handle.net/2299/19659
dc.description© 2017 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission.
dc.description.abstractIn cloud service resource management system, complexity limits the system’s ability to better satisfy the application’s QoS requirements, e.g. cost budget, average response time and reliability. Numerousness, diversity, variety, uncertainty, etc. are some of the complexity factors which lead to the variation between expected plan and actual running performance of cloud applications. In this paper, after defining the complexity clearly, we identify the origin of complexity in cloud service resource management system through the study of ”Local Activity Principle”. In order to manage complexity, an Entropy-based methodology is presented to use which covers identifying, measuring, analysing and controlling (avoid and reduce) of complexity. Finally, we implement such idea in a popular cloud engine, Apache Spark, for running Analysis as a Service (AaaS). Experiments demonstrate that the new, Entropy-based resource management approach can significantly improve the performance of Spark applications. Compare with the Fair Scheduler in Apache Spark, our proposed Entropy Scheduler is able to reduce overall cost by 23%, improve the average service response time by 15% - 20% and minimized the standard deviation of service response time by 30% - 45%.en
dc.format.extent12
dc.language.isoeng
dc.relation.ispartofIEEE Transactions on Emerging Topics in Computational Intelligence
dc.rightsOpen
dc.titleEntropy4Cloud: Using Entropy-Based Complexity To Optimize Cloud Service Resource Management Computational Intelligence for Cloud Computingen
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionSchool of Computer Science
dc.description.statusPeer reviewed
dc.description.versiontypeFinal Published version
dcterms.dateAccepted2018-01-23
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.1109/TETCI.2017.2755691
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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