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Scalable traffic-aware virtual machine management for cloud data centers

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conference contribution
posted on 31.07.2018, 15:23 by Fung Po TsoFung Po Tso, Konstantinos Oikonomou, Eleni Kavvadia, Dimitrios P. Pezaros
Virtual Machine (VM) management is a powerful mechanism for providing elastic services over Cloud Data Centers (DC)s. At the same time, the resulting network congestion has been repeatedly reported as the main bottleneck in DCs, even when the overall resource utilization of the infrastructure remains low. However, most current VM management strategies are traffic-agnostic, while the few that are traffic-aware only concern a static initial allocation, ignore bandwidth oversubscription, or do not scale. In this paper we present S-CORE, a scalable VM migration algorithm to dynamically reallocate VMs to servers while minimizing the overall communication footprint of active traffic flows. We formulate the aggregate VM communication as an optimization problem and we then define a novel distributed migration scheme that iteratively adapts to dynamic traffic changes. Through extensive simulation and implementation results, we show that S-CORE achieves significant (up to 87%) communication cost reduction while incurring minimal overhead and downtime.

History

School

  • Science

Department

  • Computer Science

Published in

IEEE 34th International Conference on Distributed Computing Systems (ICDCS)

Citation

TSO, F.P. ... et al, 2014. Scalable traffic-aware virtual machine management for cloud data centers. IN: IEEE 34th International Conference on Distributed Computing Systems (ICDCS), Madrid, Spain, 30 June-3 July 2014, pp.238-247.

Publisher

© IEEE

Version

AM (Accepted Manuscript)

Publication date

2014-09-01

Notes

© 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.

ISSN

1063-6927

Language

en