软件定义卫星网络多控制器可靠部署算法

郭子桢; 梁俊; 肖楠; 陈威龙

doi:10.7652/xjtuxb202102019

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软件定义卫星网络多控制器可靠部署算法

更新时间：2025-07-09

- 软件定义卫星网络多控制器可靠部署算法
- Multi-Controller Reliable Deployment Algorithm for Software Defined Satellite Network
- 西安交通大学学报 2021年55卷第2期页码：158-165
- 作者机构：
  
  空军工程大学信息与导航学院,西安,710077
- 作者简介：
- 基金信息：
  
  : 2020-06-25。作者简介: 郭子桢(1997—),男,硕士生;梁俊(通信作者),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(61871474);军委科技委国防科技项目基金资助(2201086)
- DOI：10.7652/xjtuxb202102019
  中图分类号： TN927
- 网络首发：2021-02-10，
  
  纸质出版：2021
- 稿件说明：
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郭子桢, 梁俊, 肖楠, 等. 软件定义卫星网络多控制器可靠部署算法[J]. 西安交通大学学报, 2021,55(2):158-165.

Multi-Controller Reliable Deployment Algorithm for Software Defined Satellite Network[J]. 2021, 55(2): 158-165.
郭子桢, 梁俊, 肖楠, 等. 软件定义卫星网络多控制器可靠部署算法[J]. 西安交通大学学报, 2021,55(2):158-165. DOI： 10.7652/xjtuxb202102019.

Multi-Controller Reliable Deployment Algorithm for Software Defined Satellite Network[J]. 2021, 55(2): 158-165. DOI： 10.7652/xjtuxb202102019.

摘要

针对现有软件定义卫星网络(SDSN)多控制器部署算法忽略处理时延、导致控制器负载不均衡及现有算法网络可靠性差的问题

提出了一种SDSN多控制器可靠部署算法(MCRDA)。该算法首先根据卫星网络节点处理能力及节点、链路失效概率等参量定义了控制时延、控制链路可靠性、节点吸引度等多控制器可靠部署评价指标; 然后在网络中均匀地挑选吸引度高的节点作为控制器部署位置

待控制器位置确定后

评估每个控制器-交换机组合的控制时延及控制链路可靠性

为最优的控制器-交换机组合构建控制关系

完成多控制器部署; 最后采用人工鱼群算法优化多控制器部署效果

同时引入步长更新函数以提高人工鱼群算法的收敛速度和计算精度。仿真结果表明

相较于k-均值算法、NSGA-Ⅱ算法及SoftLEO策略

MCRDA算法将控制器负载标准差降低了25%

同时将控制时延降低了17%

并使网络可靠性提升30%以上。

Abstract

The existing software defined satellite network(SDSN)multi-controller deployment algorithms with poor network reliability usually ignore processing delay to result in load imbalance between controllers. A multi-controller reliable deployment algorithm(MCRDA)is hence proposed in this paper. The multi-controller reliable deployment evaluation indexes

such as control delay

control link reliability and node attractability

are defined based on the satellite network node processing capacity

node failure probability

link failure probability and other parameters. Then the nodes with high node attractability are uniformly selected in the network as the controller deployment locations. The controller deployment locations are once determined

the control delay and control link reliability of each controller-switch combination are evaluated

and the control relationships are constructed for the optimal controller-switch combinations to complete the deployment of multiple controllers. The artificial fish swarm algorithm is used to optimize the multi-controller deployment

and the step size updating function is introduced to improve the convergence speed and calculation accuracy of the artificial fish swarm algorithm. The experimental results and comparison with k-means algorithm

NSGA-Ⅱ algorithm and SoftLEO strategy show that the MCRDA reduces the standard deviation of controller load and control delay by 25% and 17%

and heightens the network reliability by more than 30%.

关键词

Keywords

references

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