Int. J. of Big Data Intelligence   »   2017 Vol.4, No.4

 

 

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.2017.10006847

 

Int. J. of Big Data Intelligence, 2017 Vol.4, No.4, pp.250 - 262

 

Submission date: 29 Feb 2016
Date of acceptance: 27 Sep 2016
Available online: 04 Aug 2017

 

 

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