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Adaptive live VM migration over WAN: modelingand implementation

The combination of traditional process migration and the new virtualization technology enables mobility of virtual machines and resource provisioning within data centers. While applied to wide area network (WAN), a traditional migration algorithm has to adjust itself according to the various WAN situations and VM status. This thesis identifies four performance measurements of a VM migration: total migration time, downtime, remote up time and performance degradation. It observes that the total migration time and the remote up time of traditional pre-copy over WAN is too long to tolerate. This thesis claims that even for WAN, post-copy could be used to improve the total migration time and remote up time, only by introducing tolerable, predictable and controllable performance degradation. The adaptiveness of the migration algorithm is concerned. It proposes a hybrid solution of pre-copy and post-copy, both for memory and storage, to do the migration. In the hybrid solution, a fraction of memory (Mfrac) and a fraction of storage (Sfrac) are migrated in the pre-copy and freeze-and-copy phase, and the remaining are migrated in the post-copy phase. A model-based solution with the help of profiling is proposed to adaptively find the best combination of Mfrac and Sfrac. The evaluation part suggests that the proposed solution could adapt to different application behaviors and network conditions. / published_or_final_version / Computer Science / Master / Master of Philosophy

Identiferoai:union.ndltd.org:HKU/oai:hub.hku.hk:10722/188308
Date January 2013
CreatorsZhang, Weida, 张伟达
ContributorsWang, CL
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
Source SetsHong Kong University Theses
LanguageEnglish
Detected LanguageEnglish
TypePG_Thesis
Sourcehttp://hub.hku.hk/bib/B50534270
RightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works., Creative Commons: Attribution 3.0 Hong Kong License
RelationHKU Theses Online (HKUTO)

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