dc.contributor.author | Chen, Huikai | |
dc.contributor.author | Wang, Frank Zhigang | |
dc.contributor.author | Migliavacca, Matteo | |
dc.contributor.author | Chua, Leon O. | |
dc.contributor.author | Helian, Na | |
dc.date.accessioned | 2018-01-30T23:26:21Z | |
dc.date.available | 2018-01-30T23:26:21Z | |
dc.date.issued | 2016-09-01 | |
dc.identifier.citation | Chen , H , Wang , F Z , Migliavacca , M , Chua , L O & Helian , N 2016 , Complexity Reduction: Local Activity Ranking By Resource Entropy For QoS-aware Cloud Scheduling . in 2016 IEEE International Conference on Services Computing (SCC) . Institute of Electrical and Electronics Engineers (IEEE) , pp. 585-592 , 2016 IEEE International Conference on Services Computing , San Francisco, California , United Kingdom , 27/06/16 . https://doi.org/10.1109/SCC.2016.82 | |
dc.identifier.citation | conference | |
dc.identifier.isbn | 978-1-5090-2628-9 | |
dc.identifier.isbn | 978-1-5090-2628-9 | |
dc.identifier.other | ORCID: /0000-0001-6687-0306/work/64003369 | |
dc.identifier.uri | http://hdl.handle.net/2299/19691 | |
dc.description | © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |
dc.description.abstract | The principle of local activity originated from electronic circuits, but can easily translate into other non-electrical homogeneous/heterogeneous media. Cloud resource is an example of a locally-active device, which is the origin of complexity in cloud scheduling system. However, most of the researchers implicitly assume the cloud resource to be locally passive when constructing new scheduling strategies. As a result, their research solutions perform poorly in the complex cloud environment. In this paper, we first study several complexity factors caused by the locally-active cloud resource. And then we extended the ”Local Activity Principle” concept with a quantitative measurement based on Entropy Theory. Furthermore, we classify the scheduling system into ”Order” or ”Chaos” state with simulating complexity in the cloud. Finally, we propose a new approach to controlling the chaos based on resource’s Local Activity Ranking for QoS-aware cloud scheduling and implement such idea in Spark. Experiments demonstrate that our approach outperforms the native Spark Fair Scheduler with server cost reduced by 23%, average response time improved by 15% - 20% and standard deviation of response time minimized by 30% - 45%. Keywords—Local Activity Principle, Entropy Theory, | en |
dc.format.extent | 8 | |
dc.format.extent | 539555 | |
dc.language.iso | eng | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | |
dc.relation.ispartof | 2016 IEEE International Conference on Services Computing (SCC) | |
dc.subject | Local Activity Principle | |
dc.subject | Entropy Theory | |
dc.subject | Cloud Scheduling | |
dc.subject | Quality of Service | |
dc.subject | complex systems | |
dc.subject | Order and | |
dc.title | Complexity Reduction: Local Activity Ranking By Resource Entropy For QoS-aware Cloud Scheduling | en |
dc.contributor.institution | Centre for Computer Science and Informatics Research | |
dc.contributor.institution | School of Computer Science | |
dc.contributor.institution | Biocomputation Research Group | |
dc.identifier.url | http://ieeexplore.ieee.org/document/7557502/ | |
rioxxterms.versionofrecord | 10.1109/SCC.2016.82 | |
rioxxterms.type | Other | |
herts.preservation.rarelyaccessed | true | |