DocumentCode
1796792
Title
Scalable Traffic-Aware Virtual Machine Management for Cloud Data Centers
Author
Fung Po Tso ; Oikonomou, Kleomenis ; Kavvadia, Eleni ; Pezaros, Dimitrios P.
Author_Institution
Sch. of Comput. Sci., Univ. of Glasgow, Glasgow, UK
fYear
2014
fDate
June 30 2014-July 3 2014
Firstpage
238
Lastpage
247
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.
Keywords
cloud computing; optimisation; virtual machines; S-CORE; VM management; cloud data center; distributed migration scheme; elastic services; network congestion; optimization problem; scalable VM migration algorithm; scalable traffic-aware virtual machine management; Bandwidth; Dynamic scheduling; IP networks; Resource management; Servers; Topology; Virtual machine monitors; Communication Cost; Consolidation; Data Center Network; Migration; Scalable; Traffic-Aware; Virtual Machine;
fLanguage
English
Publisher
ieee
Conference_Titel
Distributed Computing Systems (ICDCS), 2014 IEEE 34th International Conference on
Conference_Location
Madrid
ISSN
1063-6927
Print_ISBN
978-1-4799-5168-0
Type
conf
DOI
10.1109/ICDCS.2014.32
Filename
6888900
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