Scalable traffic-aware virtual machine management for cloud data centers

Tso, F. P., Oikonomou, K., Kavvadia, E. and Pezaros, D. P. (2014) Scalable traffic-aware virtual machine management for cloud data centers. In: IEEE International Conference on Distributed Computing Systems (IEEE ICDCS), Madrid, Spain, 30 Jun - 3 Jul 2014, (doi:10.1109/ICDCS.2014.32)

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Publisher's URL: http://lsd.ls.fi.upm.es/icdcs2014/workshops

Abstract

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.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Tso, Dr Fung Po and Pezaros, Dr Dimitrios
Authors: Tso, F. P., Oikonomou, K., Kavvadia, E., and Pezaros, D. P.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Research Group:Embedded Networked and Distributed Systems (ENDS)

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
608831IMC2: Instrumentation, Measurement and Control for the CloudDimitrios PezarosEngineering & Physical Sciences Research Council (EPSRC)EP/L005255/1COM - COMPUTING SCIENCE