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Efficient live virtual machine migration for memory write-intensive workloads

delete2019-06-01
delete16
PRE
AI
C
Chunguang Li
D
Dan Feng *
Y
Yu Hua
L
Leihua Qin
DOI:10.1016/j.future.2018.12.048delete
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Abstract

Abstract

En 中文
Live migration of virtual machines (VMs) is an indispensable feature in cloud computing systems due to the use of load balancing, system maintenance, power management, etc. However, it is usually an intractable problem to migrate the VMs running memory write-intensive workloads, because they rapidly generate plenty of dirty pages, which need to be resent many times. Migration failures or compromises with large downtimes usually result in severe consequences such as the violation of service level agreements, abortion of TCP connections, etc. To solve this challenging problem, we first demonstrate a novel observation: during the pre-copy migrations, some workloads have a large portion of fake dirty pages, which are unnecessary and wasteful to be resent. After exploring how they are generated, we propose to leverage the secure hash method to avoid resending them. Besides, to guarantee the successful accomplishment of migration, we further propose the intelligent hybrid migration. Our scheme leverages heuristic and automatically switches from pre-copy to post-copy at the near-optimal moment to obtain a short post-copy duration, thus alleviating post-copy's inherent weaknesses. Evaluations show that our proposed scheme gets a significant performance improvement for VM migration. The workloads which fail to be migrated with pre-copy now accomplish migration quickly, with a total migration time from 27 s to 98 s. Besides, the intelligent hybrid migration remarkably shortens the post-copy duration by an extent from 43% to 60%, compared with the traditional hybrid method. (C) 2018 Published by Elsevier B.V.
Keywords:
VM migration
Write-intensive workloads
Fake dirty pages
Intelligent hybrid migration
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Journal

F
Future Generation Computer Systems-The International Journal of eScience
IF:
6.1
Papers:
6.9K
Citations:
2.3W

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