Xie Kang, Yang Yixian, Zhang Ling, Jing Maohua, Xin Yang, Li Zhongxian
College of Information Science and Engineering, Shandong University, Jinan 250100, China.
College of Information Science and Engineering, Shandong University, Jinan 250100, China ; Information Security Center, Beijing University of Posts and Telecommunications, Beijing 100876, China ; Northeastern University & College of Information Science and Engineering, Shenyang 110819, China.
ScientificWorldJournal. 2014;2014:829614. doi: 10.1155/2014/829614. Epub 2014 May 6.
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.
为了满足现有云计算中大规模、自动缩放和异构虚拟资源的操作监控需求，提出了一种基于细胞神经网络（CNN）的实时虚拟机（VM）迁移检测算法的新方法。通过分析检测过程，将CNN的参数关系映射为一个优化问题，其中使用基于冒泡排序的改进粒子群优化算法来解决该问题。实验结果表明，所提方法能够直观地显示VM迁移过程。与最佳拟合启发式算法相比，该方法减少了处理时间，并且新出现的证据表明，这种新方法适用于并行性和模拟超大规模集成电路（VLSI）实现，从而能够更好地执行VM迁移检测。
