Global detection of live virtual machine migration based on cellular neural networks.

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...

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Publicado en:Scientific World Journal pp. 829614 - 829615
Autores principales: Xie, Kang, Yang, Yixian, Zhang, Ling, Jing, Maohua, Xin, Yang, Li, Zhongxian
Formato: research Journal Article
Publicado: Wiley-Blackwell 2014
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Global detection of live virtual machine migration based on cellular neural networks.
      aug:
        au:
          Xie, Kang
          Yang, Yixian
          Zhang, Ling
          Jing, Maohua
          Xin, Yang
          Li, Zhongxian
        affil: College of Information Science and Engineering, Shandong University, Jinan 250100, China.
      sug:
        subj:
          Artificial Intelligence
          Neural Networks (Computer)
          Algorithms
      ab: 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.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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