基于改进蚁群算法的SDN网络负载均衡研究
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摘要
考虑到服务器处理性能的SDN网络负载均衡研究对于合理分配资源及提高服务性能具有重要意义,文中提出了基于改进蚁群算法的SDN网络负载均衡研究。首先对SDN网络结构及负载均衡进行了分析;然后根据SDN网络负载均衡的实际需求,对传统蚁群算法进行了改进,将每条链路带宽的空闲率作为蚁群算法的信息素,将计算机处理器的性能和需要传输的数据量作为启发信息,采用多重启发方式对传统蚁群算法进行改进,并对改进算法的收敛性进行了证明;最后对改进算法的性能进行验证。仿真结果表明:该算法具有收敛速度快、耗时短的优点。SDN网络负载均衡仿真实验也证明了该方法的有效性和可行性。
The study on SDN network load balancing considering server processing performance is of great significance to reasonably allocate resources and improve service performance.Therefore,this paper studied on SDN load balancing based on improved ant colony algorithm.Firstly,the structure and load balance of SDN are analyzed.Then,according to the actual demand of SDN load balancing,the traditional ant colony algorithm is improved.The idle rate of each link bandwidth is taken as the pheromone of the ant colony algorithm,the performance of computer processor and the amount of data needed to be transmitted is taken as the enlightening information,and the traditional ant colony algorithm is improved by multiple heuristics.The convergence of the improved algorithm is also proved.Finally,perfor-mance verification simulation is performed for the improved algorithm.Simulation results verify that the proposed algorithm has the advantages of fast convergence speed and short time consuming.Simulation of SDN network load balancing also proves the validity and feasibility of this method.
The study on SDN network load balancing considering server processing performance is of great significance to reasonably allocate resources and improve service performance.Therefore,this paper studied on SDN load balancing based on improved ant colony algorithm.Firstly,the structure and load balance of SDN are analyzed.Then,according to the actual demand of SDN load balancing,the traditional ant colony algorithm is improved.The idle rate of each link bandwidth is taken as the pheromone of the ant colony algorithm,the performance of computer processor and the amount of data needed to be transmitted is taken as the enlightening information,and the traditional ant colony algorithm is improved by multiple heuristics.The convergence of the improved algorithm is also proved.Finally,perfor-mance verification simulation is performed for the improved algorithm.Simulation results verify that the proposed algorithm has the advantages of fast convergence speed and short time consuming.Simulation of SDN network load balancing also proves the validity and feasibility of this method.
引文
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[16] YACINE M,DJAMILA R.High performance of Maximum Power Point Tracking Using Ant Colony algorithm in wind turbine[J].Renewable energy,2018,126:1055-1063.
[17] RABORN ANTHONY W,LEITE WALTER L.ShortForm:An R Package to Select Scale Short Forms With the Ant Colony Optimization Algorithm[J].Applied Psychol Ogical Measurement,2018,42(6):516-517.
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[19] STUTZLE T,DORIGO M.A short convergence proof for a class of ant colony optimization algorithms[J].IEEE Transactions on Evolutionary Computation,2002,6(4):358-365.
[2] SHANG F J,MAO L,GONG,W J.Service-aware adaptive link load balancing mechanism for Software-Defined Networking[J].Future Generation Computer Systems-The International Journal of Escience,2018,81:452-464.
[3] YANG X W,XU H L,HUANG L S,et al.Joint Virtual Switch Deployment and Routing for Load Balancing in SDNs[J].IEEE Journal on Selected Areas in Communications,2018,36(3):397-410.
[4] WANG H B,XU H L,LIU S,et al.Load-balancing routing in software defined networks with multiple controllers[J].Computer Networks,2018,141(4):82-91.
[5] CHIEN W C,LAI C F,CHO H H,et al.A SDN-SFC-based service-oriented load balancing for the IoT applications[J].Journal of Network and Computer Applications,2018,114:88-97.
[6] SAHOO K S,TIWARY M,S BAHOO,et al.DSSDN:Demand-supply based load balancing in Software-Defined Wide-Area Networks[J].International Journal of Network Management,2018,28(4):1-25.
[7] CHEN Y J,WANG L C,CHEN M C,et al.SDN-Enabled Traffic-Aware Load Balancing for M2M Networks[J].IEEE Internet of Things Journal,2018,5(3):1797-1806.
[8] 张敏敏,章韵,段元新.基于软件定义网络的多控制器负载均衡架构[J].计算机工程,2016,42(9):26-32.
[9] 朱世珂,束永安.基于软件定义网络的分层式控制器负载均衡机制[J].计算机应用,2017,37(12):3351-3355,3360.
[10] 柳林,周建涛.软件定义网络控制平面的研究综述[J].计算机科学,2017,44(2):75-81.
[11] SHI JG,ZHU W,JIA K Y,et al.Multi-controller Deployment Algorithm Based on Load Balance in Software Defined Network[J].Journal of Electronics&Information Technology,2018,40(2):455-461.
[12] WANG Q,GAO L R,YANG Y T,et al.A load-balanced Algorithm for Multi-Controller Placement in Software-Defined Network[J].Me Chatronic Systems and Control,2018,46(2):72-81.
[13] 胡涛,张建辉,毛明.SDN中基于迁移优化的控制器负载均衡策略[J].计算机应用研究,2018,35(2):559-563.
[14] YU G O,IVAN V C.SDN Load Balacing for Secure Networks[J].Systems and Means of Informatic,2018,28(1):123-138.
[15] ZHOU Y,ZHENG K F,NI W,et al.Elastic Switch Migration for Control Plane Load Balancing in SDN[J].IEEE Access,2018,PP(99):3909-3919.
[16] YACINE M,DJAMILA R.High performance of Maximum Power Point Tracking Using Ant Colony algorithm in wind turbine[J].Renewable energy,2018,126:1055-1063.
[17] RABORN ANTHONY W,LEITE WALTER L.ShortForm:An R Package to Select Scale Short Forms With the Ant Colony Optimization Algorithm[J].Applied Psychol Ogical Measurement,2018,42(6):516-517.
[18] SIVARAJ R,PRIYA R D.Estimation of incomplete values in heterogeneous attribute large datasets using discretized Bayesian max-min ant colony optimization[J].Knowledge and Information Systems,2018,56(2):309-334.
[19] STUTZLE T,DORIGO M.A short convergence proof for a class of ant colony optimization algorithms[J].IEEE Transactions on Evolutionary Computation,2002,6(4):358-365.