Journal
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
Volume 16, Issue 2, Pages 249-264Publisher
SPRINGER
DOI: 10.1007/s10586-011-0194-3
Keywords
Virtual machine; Live migration; Performance model; Energy
Funding
- National 973 Basic Research Program of China [2007CB310900]
- China National Natural Science Foundation (NSFC) [60973133]
- MoE-Intel Information Technology Special Research Foundation [MOE-INTEL-10-05]
- U.S. NSF [CRI-0708232, CNS-0702488, CNS-0914330, CCF-1016966]
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [0914330] Funding Source: National Science Foundation
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Live migration of virtual machine (VM) provides a significant benefit for virtual server mobility without disrupting service. It is widely used for system management in virtualized data centers. However, migration costs may vary significantly for different workloads due to the variety of VM configurations and workload characteristics. To take into account the migration overhead in migration decision-making, we investigate design methodologies to quantitatively predict the migration performance and energy consumption. We thoroughly analyze the key parameters that affect the migration cost from theory to practice. We construct application-oblivious models for the cost prediction by using learned knowledge about the workloads at the hypervisor (also called VMM) level. This should be the first kind of work to estimate VM live migration cost in terms of both performance and energy in a quantitative approach. We evaluate the models using five representative workloads on a Xen virtualized environment. Experimental results show that the refined model yields higher than 90% prediction accuracy in comparison with measured cost. Model-guided decisions can significantly reduce the migration cost by more than 72.9% at an energy saving of 73.6%.
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