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Mathematical Problems in Engineering
Volume 2014, Article ID 973069, 11 pages
Research Article

A Virtual Machine Migration Strategy Based on Time Series Workload Prediction Using Cloud Model

1Engineering Laboratory of Network and Information Security, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2Department of Mathematics and Computer Science, Nicholls State University, Thibodaux, LA 70310, USA

Received 6 June 2014; Accepted 19 August 2014; Published 28 September 2014

Academic Editor: Chuandong Li

Copyright © 2014 Yanbing Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Aimed at resolving the issues of the imbalance of resources and workloads at data centers and the overhead together with the high cost of virtual machine (VM) migrations, this paper proposes a new VM migration strategy which is based on the cloud model time series workload prediction algorithm. By setting the upper and lower workload bounds for host machines, forecasting the tendency of their subsequent workloads by creating a workload time series using the cloud model, and stipulating a general VM migration criterion workload-aware migration (WAM), the proposed strategy selects a source host machine, a destination host machine, and a VM on the source host machine carrying out the task of the VM migration. Experimental results and analyses show, through comparison with other peer research works, that the proposed method can effectively avoid VM migrations caused by momentary peak workload values, significantly lower the number of VM migrations, and dynamically reach and maintain a resource and workload balance for virtual machines promoting an improved utilization of resources in the entire data center.