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The Scientific World Journal
Volume 2014, Article ID 829614, 8 pages
Research Article

Global Detection of Live Virtual Machine Migration Based on Cellular Neural Networks

1College of Information Science and Engineering, Shandong University, Jinan 250100, China
2Information Security Center, Beijing University of Posts and Telecommunications, Beijing 100876, China
3Northeastern University & College of Information Science and Engineering, Shenyang 110819, China
4National Cybernet Security Ltd., Beijing 100088, China

Received 12 February 2014; Accepted 31 March 2014; Published 6 May 2014

Academic Editor: Antonio Puliafito

Copyright © 2014 Kang Xie 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.


In order to meet the demands of operation monitoring of large scale, autoscaling, and heterogeneous virtual resources in the existing cloud computing, a new method of live virtual machine (VM) migration detection algorithm based on the cellular neural networks (CNNs), is presented. Through analyzing the detection process, the parameter relationship of CNN is mapped as an optimization problem, in which improved particle swarm optimization algorithm based on bubble sort is used to solve the problem. Experimental results demonstrate that the proposed method can display the VM migration processing intuitively. Compared with the best fit heuristic algorithm, this approach reduces the processing time, and emerging evidence has indicated that this new approach is affordable to parallelism and analog very large scale integration (VLSI) implementation allowing the VM migration detection to be performed better.