Load balancing scheme based on multi-objective optimization for software defined network

doi:10.11772/j.issn.1001-9081.2017.06.1555

Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (6): 1555-1559.DOI: 10.11772/j.issn.1001-9081.2017.06.1555

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Load balancing scheme based on multi-objective optimization for software defined network

LIU Biguo, SHU Yong'an, FU Yinghui

School of Computer Science and Technology, Anhui University, Hefei Anhui 230601, China

Received:2016-11-09 Revised:2016-12-22 Online:2017-06-10 Published:2017-06-14
Supported by:
This work is partially supported by the Natural Science Foundation of Anhui Province (1408085MF125).

基于多目标优化的软件定义网络负载均衡方案

刘必果, 束永安, 付应辉

安徽大学计算机科学与技术学院, 安徽合肥 230601

通讯作者: 刘必果
作者简介:刘必果(1992-),女,安徽池州人,硕士研究生,主要研究方向:软件定义网络、负载均衡;束永安(1966-),男,安徽六安人,教授,博士,主要研究方向:无线网络、下一代网络体系结构;付应辉(1991-),男,安徽六安人,硕士研究生,主要研究方向:软件定义网络、负载均衡。
基金资助:
安徽省自然科学基金资助项目（1408085MF125）。

Abstract

Abstract: In order to solve the problem of load balancing in Software Defined Network (SDN) control plane, a Dynamic Switch Migration Algorithm based on Multi-objective optimization (M-DSMA) was proposed. Firstly, the mapping relationship between the switch and the controller was transformed into 0-1 matrix optimization problem. Then, the two conflicting objective functions were simultaneously optimized and controlled by the multi-objective genetic algorithm based on Non-dominated Sorting Genetic Algorithm-Ⅱ (NSGA-Ⅱ), one was the plane load balancing degree and another one was the communication overhead generated by switch migration. In the process of multi-objective optimization, the individuals were selected by using the fitness function for crossover and mutation, and then a rapid non-dominated sorting method was used to elite strategy in population. The next generation population was generated and the whole population was continually evolved, thus the global optimal solution was searched. The simulation results show that, the proposed M-DSMA can effectively balance the control plane load, and reduce the communication overhead by 30% to 50% compared with Dynamic Switch Migration Algorithm (DSMA). The proposed algorithm has the significant advantages in improving the control plane scalability.

Key words: Software Defined Network (SDN), load balancing, multi-objective optimization, Genetic Algorithm (GA), switch migration

摘要： 针对软件定义网络（SDN）中控制平面的负载均衡问题，提出了一种基于多目标优化的动态交换机迁移算法（M-DSMA）。该算法首先将交换机与控制器之间的映射关系转变为0-1矩阵优化问题；其次，通过基于NSGA-Ⅱ的多目标遗传算法同时优化控制平面负载均衡度和交换机迁移所产生的通信开销这两个相互冲突的目标。在多目标优化过程中，利用适应度函数选择个体进行交叉变异，随后采用快速非支配排序对种群进行精英策略，产生下一代种群，使得整个种群不断进化，搜索较优的解。仿真实验结果表示，相比于动态交换机迁移算法（DSMA），M-DSMA在有效均衡控制平面负载的同时，降低了30%~50%的通信开销，且在提高控制平面可扩展性方面具有明显优势。

关键词: 软件定义网络, 负载均衡, 多目标优化, 遗传算法, 交换机迁移

CLC Number:

TP393
TP18

LIU Biguo, SHU Yong'an, FU Yinghui. Load balancing scheme based on multi-objective optimization for software defined network[J]. Journal of Computer Applications, 2017, 37(6): 1555-1559.

刘必果, 束永安, 付应辉. 基于多目标优化的软件定义网络负载均衡方案[J]. 计算机应用, 2017, 37(6): 1555-1559.

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Load balancing scheme based on multi-objective optimization for software defined network

基于多目标优化的软件定义网络负载均衡方案

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