Adaptive management and multi-objective optimization of virtual machine in cloud computing based on particle swarm optimization

被引:0
作者
Shuxiang Li
Xianbing Pan
机构
[1] Chongqing University of Posts and Telecommunications,Department of Mathematics and Physics Teaching, Yitong College
[2] Chongqing University of Posts and Telecommunications,Department of Management Engineering, School of Communication
来源
EURASIP Journal on Wireless Communications and Networking | / 2020卷
关键词
Particle swarm optimization; Cloud computing; Virtual machine placement; Adaptive management; Multi-objective optimization;
D O I
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中图分类号
学科分类号
摘要
In order to improve the adaptive management ability of virtual machine placement in cloud computing, an adaptive management and multi-objective optimization method for virtual machine placement in cloud computing is proposed based on particle swarm optimization (PSO). The objective optimization model of adaptive management of virtual machine placement in cloud computing is constructed by particle swarm evolution, and the global optimization control of adaptive management of virtual machine placement in cloud computing is carried out by introducing extremum perturbation operator. The global dynamic objective function of particle swarm optimization is constructed, and the global optimal solution of virtual machine in cloud computing is found by deconvolution algorithm, and the optimal position of particle swarm is searched in two-dimensional space. The multi-objective optimization problem of adaptive management of virtual machine placement is transformed into particle swarm optimization problem to realize adaptive management and multi-objective optimization of virtual machine placement in cloud computing. Simulation results show that the adaptive management of virtual machine placement in cloud computing using this method has better global optimization ability, better convergence of particle swarm optimization, and better performance of multi-objective optimization.
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[1]  
Hongwei H(2017)Density clustering method based on complex learning classification system J. Comput. Appl. 37 3207-3211
[2]  
Xiaotian GE(2016)Empirical analysis of offshore and onshore RMB interest rate pricing: Based on the spillover index and its dynamic path Int. Financ. Res. 350 86-96
[3]  
Xuansong C(2016)Research on the policy effect of incremental expansion of margin and securities lending: Based on the multi period DID model and Hausman’s test Int. Financ. Res. 349 85-96
[4]  
Hao C(2014)Short-sales constraints and liquidity change: Cross-sectional evidence from the Hong Kong market Pac. Basin Financ. J. 26 98-122
[5]  
Ping C(2016)Study on the influence of the introduction of leverage ratio on the asset structure of commercial banks Int. Financ. Res. 350 52-60
[6]  
Yang H(2014)Image quality assessment scheme with topographic independent components analysis for sparse feature extraction Electron. Lett. 50 509-510
[7]  
Lei Y(2014)A new survey on block matching algorithms in video coding Int. J. Eng. Res. 3 121-125
[8]  
Xin YW(2016)Lung segmentation method based on 3D region growing method and improved convex hull algorithm JEIT 38 2358-2364
[9]  
Bai M(2015)An improved fletcher-reeves conjugate gradient method with descent property Acta Mathematicae Applicatae Sinica 38 89-97
[10]  
Qin Y(2015)The asymptotic relation between the maxima and sums of discrete and continuous time strongly dependent Gaussian processes Acta Mathematicae Applicatae Sinica 38 27-36