Exploiting Data Deduplication to Accelerate Live Virtual Machine Migration

Xiang, Zhang; Huo, Zhigang; Ma, Jie; Meng, Dan

doi:10.1109/cluster.2010.17

Cited by 95 publications

(50 citation statements)

References 14 publications

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“…Separate work has demonstrated that compression can reduce migration time down to seven seconds [45] and RDMA on a 10Gbps network can reduce it further to two seconds [27]. Simultaneous migrations and the congestion of the network by workload traffic will also affect the migration time.…”

Section: Discussionmentioning

confidence: 99%

Using Network Knowledge to Improve Workload Performance in Virtualized Data Centers

Erickson

Heller

McKeown

et al. 2014

2014 IEEE International Conference on Cloud Engineering

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Abstract-The scale and expense of modern data centers motivates running them as efficiently as possible. This paper explores how virtualized data center performance can be improved when network traffic and topology data informs VM placement. Our practical heuristics, tested on network-heavy, scale-out workloads in an 80 server cluster, improve overall performance by up to 70% compared to random placement in a multi-tenant configuration.

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Section: Discussionmentioning

confidence: 99%

Using Network Knowledge to Improve Workload Performance in Virtualized Data Centers

Erickson

Heller

McKeown

et al. 2014

2014 IEEE International Conference on Cloud Engineering

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“…Delta page transfer [6,32,26] optimizes the transmission of dirtied pages by sending difference between old pages and new pages, instead of sending full copies of new pages. Data deduplication [34,32] detects identical data inside the memory and disk of a single virtual machine and transfers this data only once.…”

Section: Background and Related Workmentioning

confidence: 99%

Shrinker: Improving Live Migration of Virtual Clusters over WANs with Distributed Data Deduplication and Content-Based Addressing

Riteau

Morin

Priol

2011

Euro-Par 2011 Parallel Processing

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Abstract. Live virtual machine migration is a powerful feature of virtualization technologies. It enables efficient load balancing, reduces energy consumption through dynamic consolidation, and makes infrastructure maintenance transparent to users. While live migration is available across wide area networks with state of the art systems, it remains expensive to use because of the large amounts of data to transfer, especially when migrating virtual clusters rather than single virtual machine instances. As evidenced by previous research, virtual machines running identical or similar operating systems have significant portions of their memory and storage containing identical data. We propose Shrinker, a live virtual machine migration system leveraging this common data to improve live virtual cluster migration between data centers interconnected by wide area networks. Shrinker detects memory pages and disk blocks duplicated in a virtual cluster to avoid sending multiple times the same content over WAN links. Virtual machine data is retrieved in the destination site with distributed content-based addressing. We implemented a prototype of Shrinker in the KVM hypervisor and present a performance evaluation in a distributed environment. Experiments show that it reduces both total data transferred and total migration time.

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“…Therefore, it is necessary to ensure trade-off between resource overhead and compression effects to achieve efficient migration. [31][32].  Post-Copy Approach: This approach postpones the memory page transfer phase until after the VM's CPU state has been transferred to the target PM and resumed there.…”

Section: Optimizations Of Live Migrationmentioning

confidence: 99%

“…This improves the performance of multiple VM migration. This is known as "Gang Scheduling" of virtual machines [27][28][29][30][31][32]. It optimizes both memory and network overhead of migration.…”

Section: Multiple Vm Migrationsmentioning

confidence: 99%