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Self-Adaptive Capacity Controller: A Reinforcement Learning Approach
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2016 (English)In: 2016 IEEE INTERNATIONAL CONFERENCE ON AUTONOMIC COMPUTING (ICAC), LOS ALAMITOS: IEEE Computer Society, 2016, 233-234 p.Conference paper, Published paper (Refereed)
Abstract [en]

Interference between co-located VMs may lead to performance fluctuations and degradation. To limit this problem, VMs access to physical resources needs to be controlled to ensure certain degree of isolation among them. This mapping between virtual and physical resources must be performed in a dynamic way so that it can be adaptive to the changing applications requirements, as well as to the different set of co-located VMs. To address this problem we propose a self-adaptive fuzzy Q-learning capacity controller that proactively readjusts the isolation degree based on applications performance. Our evaluation demonstrates a reduction into VMs interference and an increment on the overall utilization, while still ensuring critical applications performance, and providing more resources to non-critical applications.

Place, publisher, year, edition, pages
LOS ALAMITOS: IEEE Computer Society, 2016. 233-234 p.
Series
Proceedings of the International Conference on Autonomic Computing, ISSN 2474-0756
National Category
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-130487DOI: 10.1109/ICAC.2016.51ISI: 000390681200035ISBN: 978-1-5090-1653-2 (print)OAI: oai:DiVA.org:umu-130487DiVA: diva2:1067095
Conference
13th IEEE International Conference on Autonomic Computing (ICAC), JUL 17-22, 2016, Wurzburg, GERMANY
Available from: 2017-01-20 Created: 2017-01-20 Last updated: 2017-01-20Bibliographically approved

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CiteExportLink to record
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Citation style
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