基于遗传算法的大数据资源分配算法
|
摘要
针对云计算数据中心资源分配算法的资源利用率较低的问题,提出了一种基于改进遗传算法的云计算数据中心资源分配算法。首先,遍历每个服务器与虚拟机的需求,使用启发式贪婪算法,按照遗传算法搜索的最优虚拟机顺序将虚拟机分配至物理服务器;然后,将染色体对应的虚拟机顺序转化为装箱问题的装箱解,根据适应度值搜索资源池的最优顺序;最终,最小化云计算物理服务器的数量,减少了物理服务器的资源浪费量。基于不同虚拟机规模进行了仿真实验,结果显示:本算法对于多维装箱问题具有较好的性能,在云计算资源分配方面也获得了较好的资源利用率。
Concerning the problem that the current resource allocation algorithms of cloud computing data center perform low resource utilization ratio,a greedy resource allocation algorithm of cloud computing based on improved genetic algorithm is proposed. Firstly,each server and the requirement of each virtual machine are scanned,heuristic greedy algorithm is used to assign the virtual machines to each physic server according to the optimal virtual machine order optimized by genetic algorithm.Then,the virtual machine orders corresponding to the chromosomes are transformed to the packing solution of packing problem,the optimal order of resource pool is searched according to the fitness values. Lastly,the amount of physic servers of clouding computing is minimized and the resource wastage of physic servers are reduced. The simulation experimental results based on different virtual machine scales show that the proposed algorithm outperforms the other algorithms to multi dimensional packing problems,and realizes a better resource usage ratio than the other algorithms to cloud computing resource allocation application.
Concerning the problem that the current resource allocation algorithms of cloud computing data center perform low resource utilization ratio,a greedy resource allocation algorithm of cloud computing based on improved genetic algorithm is proposed. Firstly,each server and the requirement of each virtual machine are scanned,heuristic greedy algorithm is used to assign the virtual machines to each physic server according to the optimal virtual machine order optimized by genetic algorithm.Then,the virtual machine orders corresponding to the chromosomes are transformed to the packing solution of packing problem,the optimal order of resource pool is searched according to the fitness values. Lastly,the amount of physic servers of clouding computing is minimized and the resource wastage of physic servers are reduced. The simulation experimental results based on different virtual machine scales show that the proposed algorithm outperforms the other algorithms to multi dimensional packing problems,and realizes a better resource usage ratio than the other algorithms to cloud computing resource allocation application.
引文
[1]赵久彬,刘元雪,宋林波,等.大数据关键技术在滑坡监测预警系统中的应用[J].重庆理工大学学报(自然科学),2018(2):182-190.
[2]余晓杉,王琨,顾华玺,等.云计算数据中心光互连网络:研究现状与趋势[J].计算机学报,2015,38(10):1924-1945.
[3]丁丁,罗四维,艾丽华.基于双向拍卖的适应性云计算资源分配机制[J].通信学报,2012(s1):132-140.
[4]卢浩洋,陈世平.基于包簇映射的云计算资源分配框架[J].计算机应用,2016,36(10):2704-2709.
[5]张小庆,岳强.协作式云资源博弈分配[J].计算机应用,2014,34(7):1848-1851.
[6]DAHMANI N,CLAUTIAUX F,KRICHEN S,et al.Selfadaptive metaheuristics for solving a multi-objective 2-dimensional vector packing problem[J].Applied Soft Computing Journal,2014,16(3):124-136.
[7]STILLWELL M,SCHANZENBACH D,VIVIEN F,et al.Resource allocation algorithms for virtualized service hosting platforms[J].Journal of Parallel&Distributed Computing,2010,70(9):962-974.
[8]SMITH J W,SOMMERVILLE I.Understanding Tradeoffs between Power Usage and Performance in a Virtualized Environment[C]//IEEE Sixth International Conference on Cloud Computing.USA:IEEE,2013:725-731.
[9]MANN ZA.A taxonomy for the virtual machine allocation problem[J].International Journal of Mathematical Models and Methods in Applied Sciences,2015,9:269-276.
[10]金伟健,王春枝.基于蝙蝠算法的云计算资源分配研究[J].计算机应用研究,2015,32(4):1184-1187.
[11]王金海,黄传河,王晶,等.异构云计算体系结构及其多资源联合公平分配策略[J].计算机研究与发展,2015,52(6):1288-1302.
[12]张秋明.基于改进蚁群算法的云计算任务调度[J].电子技术应用,2015,41(2):120-122.
[13]PORTALURI G,GIORDANO S,KLIAZOVICH D,et al.A power efficient genetic algorithm for resource allocation in cloud computing data centers[C]//International Conference on Cloud NETWORKING.USA:IEEE,2014:58-63.
[14]WILCOX D,MCNABB A,SEPPI K.Solving virtual machine packing with a Reordering Grouping Genetic Algorithm[C]//IEEE Congress of Evolutionary Computation.USA:IEEE,2011:362-369.
[15]YI P,DING H,RAMAMURTHY B.Budget-Optimized Network-Aware Joint Resource Allocation in Grids/Clouds Over Optical Networks[J].Journal of Lightwave Technology,2016,34(16):3890-3900.
[16]MASSON R,VIDAL T,MICHALLET J,et al.An iterated local search heuristic for multi-capacity bin packing and machine reassignment problems[J].Expert Systems with Applications,2013,40(13):5266-5275.
[17]XU B,PENG Z,XIAO F,et al.Dynamic deployment of virtual machines in cloud computing using multi-objective optimization[J].Soft Computing,2015,19(8):2265-2273.
[2]余晓杉,王琨,顾华玺,等.云计算数据中心光互连网络:研究现状与趋势[J].计算机学报,2015,38(10):1924-1945.
[3]丁丁,罗四维,艾丽华.基于双向拍卖的适应性云计算资源分配机制[J].通信学报,2012(s1):132-140.
[4]卢浩洋,陈世平.基于包簇映射的云计算资源分配框架[J].计算机应用,2016,36(10):2704-2709.
[5]张小庆,岳强.协作式云资源博弈分配[J].计算机应用,2014,34(7):1848-1851.
[6]DAHMANI N,CLAUTIAUX F,KRICHEN S,et al.Selfadaptive metaheuristics for solving a multi-objective 2-dimensional vector packing problem[J].Applied Soft Computing Journal,2014,16(3):124-136.
[7]STILLWELL M,SCHANZENBACH D,VIVIEN F,et al.Resource allocation algorithms for virtualized service hosting platforms[J].Journal of Parallel&Distributed Computing,2010,70(9):962-974.
[8]SMITH J W,SOMMERVILLE I.Understanding Tradeoffs between Power Usage and Performance in a Virtualized Environment[C]//IEEE Sixth International Conference on Cloud Computing.USA:IEEE,2013:725-731.
[9]MANN ZA.A taxonomy for the virtual machine allocation problem[J].International Journal of Mathematical Models and Methods in Applied Sciences,2015,9:269-276.
[10]金伟健,王春枝.基于蝙蝠算法的云计算资源分配研究[J].计算机应用研究,2015,32(4):1184-1187.
[11]王金海,黄传河,王晶,等.异构云计算体系结构及其多资源联合公平分配策略[J].计算机研究与发展,2015,52(6):1288-1302.
[12]张秋明.基于改进蚁群算法的云计算任务调度[J].电子技术应用,2015,41(2):120-122.
[13]PORTALURI G,GIORDANO S,KLIAZOVICH D,et al.A power efficient genetic algorithm for resource allocation in cloud computing data centers[C]//International Conference on Cloud NETWORKING.USA:IEEE,2014:58-63.
[14]WILCOX D,MCNABB A,SEPPI K.Solving virtual machine packing with a Reordering Grouping Genetic Algorithm[C]//IEEE Congress of Evolutionary Computation.USA:IEEE,2011:362-369.
[15]YI P,DING H,RAMAMURTHY B.Budget-Optimized Network-Aware Joint Resource Allocation in Grids/Clouds Over Optical Networks[J].Journal of Lightwave Technology,2016,34(16):3890-3900.
[16]MASSON R,VIDAL T,MICHALLET J,et al.An iterated local search heuristic for multi-capacity bin packing and machine reassignment problems[J].Expert Systems with Applications,2013,40(13):5266-5275.
[17]XU B,PENG Z,XIAO F,et al.Dynamic deployment of virtual machines in cloud computing using multi-objective optimization[J].Soft Computing,2015,19(8):2265-2273.