一种结合遗传算法和聚类的软件定义网络控制器优化部署机制  

An optimal placement mechanism for software-defined networking controllers based on genetic algorithm and clustering

作　　者：王冰彬 唐震洲 WANG Bing-bin;TANG Zhen-zhou(Wenzhou Key Laboratory for Intelligent Networking,Wenzhou University,Wenzhou 325035,China)

机构地区：[1]温州大学温州市智能网络重点实验室,浙江温州325035

出　　处：《计算机工程与科学》2024年第11期1971-1978,共8页Computer Engineering & Science

基　　金：浙江省自然科学基金(LZ20F010008)。

摘　　要：对于逻辑上集中式和物理上分布式的多控制器软件定义网络SDN,控制器的放置直接影响网络的性能,包括时延、负载均衡等。因此,控制器放置问题MCP是软件定义网络中的一个非常重要的问题。基于上述分析,提出了一种融合遗传算法和k-medoid聚类算法的启发式SDN MCP机制,称为GA-K-Medoids MCP机制,旨在最小化控制器与交换机之间以及不同控制器之间的传播时延,采用Internet2 OS3E和Palmetto 2种常见的网络拓扑对所提出的MCP机制进行了性能评估,并与其他机制进行了对比。仿真结果表明,GA-K-Medoids MCP能够为多控制器软件定义网络提供有效的低延迟的控制器部署方案。In a logically centralized but physically distributed multi-controller software-defined networking(SDN)environment,the placement of controllers directly impacts network performance,including latency and load balancing.Therefore,the multiple controllers placement(MCP)problem is a crucial issue in SDN.Based on this analysis,a heuristic SDN MCP mechanism that integrates the genetic algorithm(GA)and k-medoid clustering algorithm,termed the GA-K-Medoids MCP mechanism,is proposed.This mechanism aims to minimize the propagation delay between controllers and switches,as well as among controllers.The performance of the proposed MCP mechanism is evaluated using two common network topologies,Internet2 OS3E and Palmetto,and compared with other mechanisms.Simulation results demonstrate that the GA-K-Medoids MCP can provide an effective and low-latency controller placement solution for multi-controller SDN.

关 键 词：SDN 进化算法 多控制器部署 

分 类 号：TP342.3[自动化与计算机技术—计算机系统结构]