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MODECP:一种基于多目标的方法,用于解决软件定义网络中的分布式控制器放置问题。

MODECP: A Multi-Objective Based Approach for Solving Distributed Controller Placement Problem in Software Defined Network.

作者信息

Liao Chenxi, Chen Jia, Guo Kuo, Liu Shang, Chen Jing, Gao Deyun

机构信息

The School Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.

出版信息

Sensors (Basel). 2022 Jul 22;22(15):5475. doi: 10.3390/s22155475.

Abstract

Software-Defined Network is an emerging networking paradigm that enables intelligent and flexible network management. Specifically, the design of the control plane is crucial. Therefore, in order to avoid a single point of failure, multiple controllers are deployed constantly in a distributed manner on the control plane. In this paper, we propose a controller placement approach based on multiple objectives (MODECP), including network delay, network security, load-balancing rate, and link occupancy. In the controller placement stage, an improved multi-objective differential evolution algorithm is proposed to search for controllers' positions and assign switches to controllers reasonably. Furthermore, an improved affinity propagation algorithm is proposed to obtain the number of controllers placed in the network partition stage, comprehensively considering the delay, node security, and load. Simulations are performed based on several topologies from Internet Topology Zoo. Extensive results show that the proposed algorithm can realize trade-offs among multiple objectives and improve network performance in delay, security, controller load, and link occupancy compared to the single-objective based approach. Moreover, compared with the genetic algorithm and random placement algorithm, the proposed algorithm performs better with low latency, high security, low load rate, and low link overhead.

摘要

软件定义网络是一种新兴的网络范式,它能够实现智能且灵活的网络管理。具体而言,控制平面的设计至关重要。因此,为了避免单点故障,在控制平面上不断以分布式方式部署多个控制器。在本文中,我们提出了一种基于多目标的控制器放置方法(MODECP),包括网络延迟、网络安全、负载均衡率和链路占用情况。在控制器放置阶段,提出了一种改进的多目标差分进化算法来搜索控制器的位置,并合理地将交换机分配给控制器。此外,提出了一种改进的亲和传播算法,以在网络分区阶段获得放置的控制器数量,综合考虑延迟、节点安全性和负载。基于互联网拓扑动物园的几种拓扑进行了仿真。大量结果表明,与基于单目标的方法相比,所提出的算法能够在多个目标之间实现权衡,并在延迟、安全性、控制器负载和链路占用方面提高网络性能。此外,与遗传算法和随机放置算法相比,所提出的算法在低延迟、高安全性、低负载率和低链路开销方面表现更好。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70a4/9331998/8cb47a703f36/sensors-22-05475-g001.jpg

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