Autonomous learning for efficient resource utilization of dynamic VM migration
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, H.W. | - |
dc.contributor.author | Kwak, H. | - |
dc.contributor.author | Sohn, A. | - |
dc.contributor.author | Chung, K. | - |
dc.date.available | 2019-04-10T11:19:59Z | - |
dc.date.created | 2018-04-17 | - |
dc.date.issued | 2008 | - |
dc.identifier.isbn | 9781605581583 | - |
dc.identifier.uri | http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/33655 | - |
dc.description.abstract | Dynamic migration of virtual machines on a cluster of physical machines is designed to maximize resource utilization by balancing loads across the cluster. When the utilization of a physical machine is beyond a fixed threshold, the machine is deemed overloaded. A virtual machine is then selected within the overloaded physical machine for migration to a lightly loaded physical machine. Key to such threshold-based VM migration is to determine when to move which VM to what physical machine, since wrong or inadequate decisions can cause unnecessary migrations that would adversely affect the overall performance. We present in this paper a learning framework that autonomously finds and adjusts thresholds at runtime for different computing requirements. Central to our approach is the previous history of migrations and their effects before and after each migration in terms of standard deviation of utilization. We set up an experimental environment that consists of extensive real world benchmarking problems and a cluster of 16 physical machines each of which has on average eight virtual machines. We demonstrate through experimental results that our approach autonomously finds thresholds close to the optimal ones for different computing scenarios and that such varying thresholds yield an optimal number of VM migrations for maximizing resource utilization. | - |
dc.relation.isPartOf | Proceedings of the International Conference on Supercomputing | - |
dc.title | Autonomous learning for efficient resource utilization of dynamic VM migration | - |
dc.type | Conference | - |
dc.identifier.doi | 10.1145/1375527.1375556 | - |
dc.type.rims | CONF | - |
dc.identifier.bibliographicCitation | 22nd ACM International Conference on Supercomputing, ICS'08, pp.185 - 194 | - |
dc.description.journalClass | 2 | - |
dc.identifier.scopusid | 2-s2.0-57349141408 | - |
dc.citation.conferenceDate | 2008-06-07 | - |
dc.citation.conferencePlace | Island of Kos | - |
dc.citation.endPage | 194 | - |
dc.citation.startPage | 185 | - |
dc.citation.title | 22nd ACM International Conference on Supercomputing, ICS'08 | - |
dc.contributor.affiliatedAuthor | Chung, K. | - |
dc.type.docType | Conference Paper | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
Soongsil University Library 369 Sangdo-Ro, Dongjak-Gu, Seoul, Korea (06978)02-820-0733
COPYRIGHT ⓒ SOONGSIL UNIVERSITY, ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.