Title: S³R: storage-sensitive services redeployment in the cloud

Authors: Huining Yan; Yiming Zhang; Huaimin Wang; Bo Ding; Haibo Mi

Addresses: National Key Laboratory for Parallel and Distributed Processing, College of Computer Science, National University of Defense Technology, Changsha 410073, China ' National Key Laboratory for Parallel and Distributed Processing, College of Computer Science, National University of Defense Technology, Changsha 410073, China ' National Key Laboratory for Parallel and Distributed Processing, College of Computer Science, National University of Defense Technology, Changsha 410073, China ' National Key Laboratory for Parallel and Distributed Processing, College of Computer Science, National University of Defense Technology, Changsha 410073, China ' National Key Laboratory for Parallel and Distributed Processing, College of Computer Science, National University of Defense Technology, Changsha 410073, China

Abstract: Services redeployment is one of the critical techniques for energy-efficiency in cloud data centres. In recent years, cloud providers have been providing local storage for cloud services, since it offers a better performance with identified price. Nevertheless, most existing work did not consider the problems introduced by utilising local storage, e.g., migrating much more data, and therefore consuming much more migration time and network bandwidth. Meanwhile, instance migration is a costly operation, the number of migrated instances must be considered. However, the data size and the number of instances on servers are not often accordant, and therefore a tradeoff should be made. To address this problem, this paper proposes S³R, a storage-sensitive services redeployment approach. S³R firstly builds a tradeoff model to estimate the release cost for each server, and then adopts a FFD-based heuristic algorithm to migrate/redeploy instances. Evaluation results on production traces demonstrate the effectiveness of S³R.

Keywords: cloud computing; energy efficiency; storage-sensitive; services redeployment.

DOI: 10.1504/IJBDI.2017.086959

International Journal of Big Data Intelligence, 2017 Vol.4 No.4, pp.250 - 262

Received: 28 Mar 2016
Accepted: 27 Sep 2016
Published online: 03 Oct 2017 *

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