基于多目标离散差分进化的网络感知虚拟机放置算法
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摘要
针对云数据中心网络内部流量快速增长导致链路拥塞及全网通信代价过高的问题,提出一种改进的网络感知虚拟机放置算法。根据虚拟机的硬件资源需求,通过种群初始化提高算法收敛速度,采用离散差分变异和交叉操作保证种群多样性。综合考虑通信代价、最大链路利用率、硬件资源约束违反度和链路容量约束违反度4项指标,提出基于ε松弛的多子群精英选择策略,选择最优虚拟机放置方案,增强算法全局搜索能力。仿真结果表明,该算法能够有效降低全网通信代价,并实现网络负载均衡。
In view of the problems of link congestion and high network communication cost that result from the rapid growth of traffic within the cloud Data Center(DC),this paper proposes an improved network-aware virtual machine placement algorithm.According to the hardware resource requirements of virtual machines,the population initialization is used to improve the convergence speed of the proposed algorithm.Then,discrete differential mutation and crossover operations are used to ensure the diversity of the population.With a comprehensive consideration of 4 evaluating indicators including communication cost,maximum link utilization,hardware resource constraint violation degree and link capacity constraint violation degree,this paper proposes a multiple subgroup elitist selection strategy based on ε relaxation to select the optimal virtual machine placement scheme and enhance the global exploitation ability.Simulation results show that the proposed algorithm can effectively reduce the network communication cost and achieve the load balancing of network.
In view of the problems of link congestion and high network communication cost that result from the rapid growth of traffic within the cloud Data Center(DC),this paper proposes an improved network-aware virtual machine placement algorithm.According to the hardware resource requirements of virtual machines,the population initialization is used to improve the convergence speed of the proposed algorithm.Then,discrete differential mutation and crossover operations are used to ensure the diversity of the population.With a comprehensive consideration of 4 evaluating indicators including communication cost,maximum link utilization,hardware resource constraint violation degree and link capacity constraint violation degree,this paper proposes a multiple subgroup elitist selection strategy based on ε relaxation to select the optimal virtual machine placement scheme and enhance the global exploitation ability.Simulation results show that the proposed algorithm can effectively reduce the network communication cost and achieve the load balancing of network.
引文
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[2] 邓罡,龚正虎,王宏,等.现代数据中心网络资源管理技术分析与综述[J].通信学报,2014,35(2):166-181.
[3] 李铭夫,毕经平,李忠诚.资源调度等待开销感知的虚拟机整合[J].软件学报,2014,25(7):1388-1402.
[4] LARUMBE F,SANSó B.Elastic,on-line and network aware virtual machine placement within a data center[C]//Proceedings of 2017 IFIP/IEEE Symposium on Integrated Network and Service Management Lisbon.Washington D.C.,USA:IEEE Press,2017:28-36.
[5] MENG Xiaoqiao,PAPPAS V,ZHANG Li.Improving the scalability of data center networks with traffic-aware virtual machine placement[C]//Proceedings of IEEE International Conference on Computer Communications.Washington D.C.,USA:IEEE Press,2010:1-9.
[6] ROCHA L A,VERDI F L.A network-aware optimization for VM placement[C]//Proceedings of IEEE International Conference on Advanced Information Networking and Applications.Washington D.C.,USA:IEEE Press,2015:619-625.
[7] SHABEERA T P,KUMAR S D M,SALAM S M,et al.Optimizing VM allocation and data placement for data-intensive applications in cloud using ACO metaheuristic algorithm[J].Engineering Science and Technology,2016,20(2):616-628.
[8] TSENG F H,JHENG Y M,CHOU L D,et al.Link-aware virtual machine placement for cloud services based on service-oriented architecture[J].IEEE Transactions on Cloud Computing,2017(99):1-10.
[9] BIRAN O,CORRADI A,FANELLI M,et al.A stable network-aware VM placement for cloud systems[C]//Proceedings of IEEE/ACM International Symposium on Cluster,Cloud and Grid Computing.Washington D.C.,USA:IEEE Press,2012:498-506.
[10] WEN Xitao,CHEN Kai,CHEN Yan,et al.VirtualKnotter:online virtual machine shuffling for congestion resolving in virtualized datacenter[C]//Proceedings of IEEE International Conference on Distributed Computing Systems.Washington D.C.,USA:IEEE Press,2012:12-21.
[11] DONG Jiankang,WANG Hongbo,LI Yangyang,et al.Virtual machine placement optimizing to improve network performance in cloud data centers[J].The Journal of China Universities of Posts and Telecommunications,2014,21(3):62-70.
[12] STORN R,PRICE K.Differential evolution——a simple and efficient heuristic for global optimization over continuous spaces[J].Journal of Global Optimization,1997,11(4):341-359.
[13] 毕晓君,张磊.基于自适应ε截断策略的约束多目标优化算法[J].电子与信息学报,2016,38(8):2047-2053.
[14] PALAKONDA V,MALLIPEDDI R.Pareto dominance-based algorithms with ranking methods for many-objective optimization[J].IEEE Access,2017,5:11043-11053.
[15] CAI Zixiang,WANG Yong.A multiobjective optimization-based evolutionary algorithm for constrained optimi-zation[J].IEEE Transactions on Evolutionary Computation,2006,10(6):658-675.
[16] DEB K,PARTAP A,AGARWAL S,et al.A fast and elitist multiobjective genetic algorithm:NSGA-II[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
[17] KANAGAVELU R,LEE B S,LE N T D,et al.Virtual machine placement with two-path traffic routing for reduced congestion in data center networks[J].Computer Communications,2014,53(C):1-12.
[18] YE Xin,YIN Yanli,LAN Lan.Energy-efficient many-objective virtual machine placement optimization in a cloud computing environment[J].IEEE Access,2017,5:16006-16020.