The Community for Technology Leaders
2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) (2018)
Washington, DC, USA
May 1, 2018 to May 4, 2018
ISBN: 978-1-5386-5815-4
pp: 472-481
Data deduplication has been widely introduced to effectively reduce storage requirement of virtual machine (VM) images running on VM servers in the virtualized cloud platforms. Nevertheless, the existing state-of-the-art deduplication for VM images approaches can not sufficiently exploit the potential of underlying hardware with consideration of the interference of deduplication on the foreground VM services, which could affect the quality of VM services. In this paper, we present HPDV, a highly parallel deduplication cluster for VM images, which well utilizes the parallelism to achieve high throughput with minimum interference on the foreground VM services. The main idea behind HPDV is to exploit idle CPU resource of VM servers to parallelize the compute-intensive chunking and fingerprinting, and to parallelize the I/O-intensive fingerprint indexing in the deduplication servers by dividing the globally shared fingerprint index into multiple independent sub-indexes according to the operating systems of VM images. To ensure the quality of VM services, a resource-aware scheduler is proposed to dynamically adjust the number of parallel chunking and fingerprinting threads according to the CPU utilization of VM servers. Our evaluation results demonstrate that compared to a state-of-the-art deduplication system for VM images called Light, HPDV achieves up to 67% deduplication throughput improvement.
cloud computing, data compression, pattern clustering, scheduling, storage management, virtual machines, virtualisation

C. Lin, Q. Cao, J. Huang, J. Yao, X. Li and C. Xie, "HPDV:A Highly Parallel Deduplication Cluster for Virtual Machine Images," 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), Washington, DC, USA, 2018, pp. 472-481.
98 ms
(Ver 3.3 (11022016))