NDLI: Reducing Energy Consumption by Load Aggregation with an Optimized Dynamic Live Migration of Virtual Machines

Content Provider	IEEE Xplore Digital Library
Author	Versick, D. Tavangarian, D.
Copyright Year	2010
Abstract	Energy consumption of data centers has been in-creasing continuously during the last years due to rising demands of computational power especially in current Grid- and Cloud-Computing systems. One promising approach of reducing this energy consumption is the consolidation of servers by virtualization. Many low loaded computer systems are virtualized and run on few physical servers for reducing the number of energy-consuming computers. At present this consolidation is usually done statically, thus, the administrator of a data center manually migrates many virtual machines with low load onto one physical server which may lead to overloading when the workload is rising unexpectedly. Dynamic server migration that adapts the number of running physical machines to the current workload overcomes these problems. Physical machines can be highly loaded and in case of further rising load virtual machines are migrated to other physical server systems that have been switched on. Such dynamic load aggregation approaches are rarely used and typically only consider few criteria for migration. This paper presents a classification of migration criteria for live migration of virtual machines in load aggregation environments and proposes an algorithm for combining many different kinds of migration criteria to a clustering-based metric. Thus, the novel load aggregation algorithm optimizes energy consumption as well as other migration criteria like runtime performance of applications.
Starting Page	164
Ending Page	170
File Size	1555798
Page Count	7
File Format	PDF
ISBN	9781424485383
DOI	10.1109/3PGCIC.2010.29
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-11-04
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Server consolidation Energy consumption Clustering algorithms Virtual machining Software Hardware Energy efficiency Partitioning algorithms Servers Live migration Virtualization Load aggregation
Content Type	Text
Resource Type	Article

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