Title: Parallel plants collaborative growth algorithm for virtual machine migration

Authors: Fei Wang; Anting Chen; Yuanjun Laili; Lin Zhang; Peng Wan; Dongming Zhao; Fei Tao

Addresses: School of Automation Science and Electronic Engineering, Beihang University, Beijing, 100191, China ' School of Automation Science and Electronic Engineering, Beihang University, Beijing, 100191, China ' School of Automation Science and Electronic Engineering, Beihang University, Beijing, 100191, China ' School of Automation Science and Electronic Engineering, Beihang University, Beijing, 100191, China ' Beijing Institute of Tracking and Telecommunications Technology, Beijing, 100094, China ' Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, Michigan, 48128, USA ' School of Automation Science and Electronic Engineering, Beihang University, Beijing, 100191, China

Abstract: With the development of cloud computing, more and more virtual computing resources are provided as dynamic services to achieve green and agile computing for different kinds of projects. Virtual machine migration, as one of the main technologies, has brought huge influences on the executive and collaborative efficiency of virtual machines. However, the existing research focuses primarily on management and scheduling of virtual machines. And several studies concerning virtual machine migration in service-oriented computing still have great limitations. Especially, with the expansion of the scale of collaborative computing tasks, the complexity, as well as the number of virtual machines increased significantly. When the pre-allocation of virtual machines becomes unsuitable due to some resource failures or long waiting queue, migration of virtual machine becomes imperative. Considering the influence of initial allocation, this paper proposes a new parallel plant collaborative growth algorithm (namely Parallel-PCGA) which combines efficient plant growth optimisation operator and ring topology-based parallelisation. It brings an effective balance in solution time and accuracy. Experimental results prove that Parallel-PCGA shows high performance in large-scale virtual machine migration in cloud computing.

Keywords: virtual machines; virtual machine migration; plant growth simulation; parallel intelligent algorithms; cloud computing; agile computing; green computing; service-oriented computing; plant growth optimisation; ring topology.

DOI: 10.1504/IJSCOM.2014.063994

International Journal of Service and Computing Oriented Manufacturing, 2014 Vol.1 No.3, pp.211 - 236

Available online: 29 Jul 2014 *

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