A random key genetic algorithm for live migration of multiple virtual machines in data centers

Sarker, Tusher Kumer & Tang, Maolin (2014) A random key genetic algorithm for live migration of multiple virtual machines in data centers. Neural Information Processing: 21st International Conference, ICONIP 2014, Proceedings, Part II [Lecture Notes in Computer Science, Volume 8835], pp. 212-220.

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Abstract

Live migration of multiple Virtual Machines (VMs) has become an integral management activity in data centers for power saving, load balancing and system maintenance. While state-of-the-art live migration techniques focus on the improvement of migration performance of an independent single VM, only a little has been investigated to the case of live migration of multiple interacting VMs. Live migration is mostly influenced by the network bandwidth and arbitrarily migrating a VM which has data inter-dependencies with other VMs may increase the bandwidth consumption and adversely affect the performances of subsequent migrations. In this paper, we propose a Random Key Genetic Algorithm (RKGA) that efficiently schedules the migration of a given set of VMs accounting both inter-VM dependency and data center communication network. The experimental results show that the RKGA can schedule the migration of multiple VMs with significantly shorter total migration time and total downtime compared to a heuristic algorithm.

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ID Code: 75626
Item Type: Journal Article
Refereed: Yes
Keywords: Live virtual machine migration, downtime, migration time, genetic algorithm
DOI: 10.1007/978-3-319-12640-1_26
ISSN: 0302-9743
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Neural Evolutionary and Fuzzy Computation (080108)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > DISTRIBUTED COMPUTING (080500) > Distributed Computing not elsewhere classified (080599)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 Springer
Deposited On: 28 Aug 2014 04:39
Last Modified: 08 Dec 2014 17:24

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