University of Hertfordshire

By the same authors

Complexity Reduction: Local Activity Ranking By Resource Entropy For QoS-aware Cloud Scheduling

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Documents

  • Huikai Chen
  • Frank Zhigang Wang
  • Matteo Migliavacca
  • Leon O. Chua
  • Na Helian
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Original languageEnglish
Title of host publication2016 IEEE International Conference on Services Computing (SCC)
PublisherIEEE
Pages585-592
Number of pages8
ISBN (Electronic)978-1-5090-2628-9
ISBN (Print)978-1-5090-2628-9
DOIs
Publication statusPublished - 1 Sep 2016
Event2016 IEEE International Conference on Services Computing - San Francisco, California , United Kingdom
Duration: 27 Jun 20162 Jul 2016
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7557343

Conference

Conference2016 IEEE International Conference on Services Computing
Abbreviated titleSCC
CountryUnited Kingdom
CitySan Francisco, California
Period27/06/162/07/16
Internet address

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,

Notes

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