Zhang Xiang, Huo Zhigang, Ma Jie, Meng Dan. Fast and Live Whole-System Migration of Virtual Machines[J]. Journal of Computer Research and Development, 2012, 49(3): 661-668.
Citation:
Zhang Xiang, Huo Zhigang, Ma Jie, Meng Dan. Fast and Live Whole-System Migration of Virtual Machines[J]. Journal of Computer Research and Development, 2012, 49(3): 661-668.
Zhang Xiang, Huo Zhigang, Ma Jie, Meng Dan. Fast and Live Whole-System Migration of Virtual Machines[J]. Journal of Computer Research and Development, 2012, 49(3): 661-668.
Citation:
Zhang Xiang, Huo Zhigang, Ma Jie, Meng Dan. Fast and Live Whole-System Migration of Virtual Machines[J]. Journal of Computer Research and Development, 2012, 49(3): 661-668.
1(National Research Center for Intelligent Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190)2(Graduate University of Chinese Academy of Sciences, Beijing 100049)
Live and whole-system migration of virtual machines is important for virtual platforms in wide-area network and local-area network, in which shared storage is not deployed. However, the size of whole-system image is often tens of gigabytes, and transferring such much data will occupy too much I/O bandwidth and have serious time overhead. A fast migration method is proposed, which includes three key technologies: file-system-aware block device migration, which utilizes the allocation bitmap of disk blocks in file system to make migration only copy used disk blocks, and reduces half of the disk image data which is transferred during the first migration phase; Xor-based compression, which utilizes the self-similarity of image data to effectively reduce the amount of transferred data in the last two migration phases with low time overhead; parallel migration, which parallelizes each migration phase and overlaps the cost of reading, writing, compressing, uncompressing, sending and receiving image data. Experimental results demonstrate that compared with traditional compression migration, the fast migration method can significantly reduce 50% of migration time, 21.68% of downtime at the best time and 14.48% of downtime on average. It can also speedup migration under extreme conditions, under which network bandwidth is limited or workload in the virtual machine is intensive.
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