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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(2): 162-167 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.106

A Novel Energy Optimized and Workload Adaptive Modeling for Live Migration

Bing Wei

Abstract—Live migration provides desirable benefits in the field of energy saving, which packs service into fewer physical servers while maintaining the performance level, most of existed work concentrates on the live migration mechanism, however, few work investigates the energy guided live migration decision making, and the varies characteristics of workload always challenges the problem. In this paper we present energy guided and workload adaptive modeling for live migration, two models are developed respectively including energy guided migration model and workload adaptive model, the former model selects the best migrating virtual machine (VM) candidate with the minimal energy consumption while the later model chooses the best migrated physical server candidate in terms of both energy and workload characteristics, furthermore, concerning the service quality, workload adaptive model also takes charge of the determination of the live migration moment. Taking workload characteristics into account in the workload adaptive model avoids considerable unnecessary live migrations and achieves stable live migration with varies workload, and thus reduces the energy usage. The experiments results show that our approach achieves significant energy saving and robust live migration.

Index Terms—Live migration, energy consumption, workload, modeling.

B. Wei is with the Department of Computer Science and Technology, Tsinghua University, Beijing 10084, China (email: weibing@csnet1.cs.tsinghua.edu.cn)


Cite: Bing Wei, "A Novel Energy Optimized and Workload Adaptive Modeling for Live Migration," International Journal of Machine Learning and Computing vol. 2, no. 2, pp. 162-167, 2012.

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