计算机科学 ›› 2015, Vol. 42 ›› Issue (Z6): 20-23.
武兴宇,孙磊,胡翠云,孙瑞辰
WU Xing-yu, SUN Lei, HU Cui-yun and SUN Rui-chen
摘要: 粒子群优化算法由于实现容易、精度高、收敛快,在解决多目标优化问题时呈现出较强的优越性。在定义匹配距离的基础上,引入粒子群优化算法思想制定虚拟机迁移选择策略,并对粒子群优化算法做出改进,引入规避列表思想,将剩余性能不满足虚拟机性能需求的服务器加入到规避列表中,以避免多个满足非劣最优解的虚拟机迁移到一台服务器,导致资源占用率超过结点资源上限。通过在CloudSim平台上与基本粒子群优化算法进行的仿真对比实验证明了本算法具有更快的收敛速度和选择速度。
[1] Nelson M,Lim Beng-Hong,Hutchins G.Fast Transparent Migration for Virtual Machines[C]∥Proceedings of USENIX ATC.2005 [2] Deb K.Multi-Objective Optimization[M].Springer:Multi-Ob-jective Optimization Using Evolutionary Algorithms,2001:13-46 [3] Deb K,Pratap A,Agarwal S,et al.A fast and elitist multi-objective genetic algorithm:NSGA-II[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197 [4] 李强,郝沁汾,肖利民,等.云计算中虚拟机放置的自适应管理与多目标优化[J].计算机学报,2011,34(12):2253-2264 [5] Kennedy J,Eberhart R.Particle swarm optimization[C]∥Proceedings of IEEE International Conference on Neural Networks.volume 4,1995 [6] 黄敏,江渝,毛安,等.基于全局最优位置自适应选取与局部搜索的多目标粒子群优化算法[J].计算机应用,2014,34(4):1074-1079 [7] 冯金芝,陈兴,郑松林.一种改进的多目标粒子群优化算法及应用[J].计算机应用研究,2014,31(3):1001-3695 [8] Xu J,Fortes J.Multi-objective virtual machine placement in virtualized data center environments[C]∥Proceedings of 2010 IEEE/ACM International Conference on Green Computing and Communications(GreenCom’2010).Hangzhou,2010:179-188 [9] 程理民,吴江,张玉林.运筹学模型与方法教程[M].北京:清华大学出版社,2002 [10] Calheiros R N,Ranjan R,De Rose C A F,et al.Cloudsim:A novel Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services [R].GRIDS-TR-2009-1.Grid Computing and Distributed Systems Laboratory,The University of Melbourne,Australia 2009 [11] Deb K.Multi-objective genetic algorithms:Problem difficultiesand construction of test problem[J].Evolutionary computing,1999,7:205-230 |
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