[1]刘振鹏,王鑫鹏,李明,等.基于时延和负载均衡的多控制器部署策略[J].郑州大学学报(工学版),2021,42(03):19.[doi:10.13705/j.issn.1671-6833.2021.03.004]
　Liu Zhenpeng,Wang Xinpeng,Li Ming,et al.Multi-controller Deployment Strategy Based on Delay and Load Balancing[J].Journal of Zhengzhou University (Engineering Science),2021,42(03):19.[doi:10.13705/j.issn.1671-6833.2021.03.004]
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基于时延和负载均衡的多控制器部署策略()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
42
期数:
2021年03期
页码:
19
栏目:
出版日期:
2021-05-10

文章信息/Info

Title:
Multi-controller Deployment Strategy Based on Delay and Load Balancing
作者:
刘振鹏; 王鑫鹏; 李明;  任少松1; 李小菲
河北大学电子信息工程学院;河北大学信息技术中心;
Author(s):
Liu Zhenpeng; Wang Xinpeng; Li Ming; Ren Shasong; Li Xiaofei;
School of Electronic Information Engineering, Hebei University; Hebei University Information Technology Center;
关键词:
Keywords:
software defined network;  controller placement;  latency;  load balancing;  particle swarm optimization
DOI:
10.13705/j.issn.1671-6833.2021.03.004
文献标志码:
A
摘要:
针对在软件定义网络SDN中多个控制器部署时面对的时间延迟和负载均衡问题在保证负载均衡的基础上以降低整个网络的时间延迟和提高整个网络性能为目标提出一种多控制器部署算法针对传统粒子群算法收敛速度慢的缺点采用改进的粒子群算法对SDN控制器进行部署在考虑平衡控制器负载能力的同时最小化交换机和控制器之间的传播时延仿真实验结果表明采用改进的粒子群算法可以在保证较高的负载均衡性能的基础上以较低的时间延迟代价获得较优的网络总体性能网络的合适度达到0.05与传统粒子群算法相比改进的粒子群算法在种群的收敛速度上提升约6.3%。
Abstract:
To address the time delay and load balancing problems faced by multiple controllers deployed in the software definition network (SDN), in this paper, a multi-controller placement algorithm is proposed to reduce the time delay between controllers, and improve the network performance on the basis of load balancing. Aiming at the slow convergence speed of traditional particle swarm optimization algorithm, this paper proposes an improved particle swarm optimization algorithm to deploy the SDN controller. The improved particle swarm optimization algorithm is used to deploy the SDN controller to minimize the propagation delay between the switch and the controller while considering the load balance of the controller.The simulation results show that the improved particle swarm optimization algorithm for controller deployment can guarantee high load balancing performance and the better overall network performance by acquire fitness about 0.05. And compared with the traditional particle swarm optimization algorithm, the improved particle swarm optimization algorithm can improve the convergence speed of the whole network about 6.3% with lower time delay.

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更新日期/Last Update: 2021-06-24