基于改进灰狼优化算法的服务功能链映射算法

张岳; 张俊楠; 吴晓春; 洪晨; 周静静

doi:10.11959/j.issn.1000-0801.2022275

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基于改进灰狼优化算法的服务功能链映射算法

研究与开发 | 更新时间：2024-06-05

- 基于改进灰狼优化算法的服务功能链映射算法
- Improved grey wolf optimization algorithm based service function chain mapping algorithm
- 电信科学 2022年38卷第11期页码：57-72
- 作者机构：
- 作者简介：
  
  [ "张岳（1998- ），女，浙江工商大学硕士生，主要研究方向为知识图谱、新一代网络技术架构" ]
  [ "张俊楠（1997- ），男，浙江工商大学硕士生，主要研究方向为新一代网络技术架构" ]
  [ "吴晓春（1983- ），女，博士，浙江工商大学高级实验师、硕士生导师，主要研究方向为新一代网络技术架构、软件定义网络、网络功能虚拟化、人工智能与网络安全的结合等" ]
  [ "洪晨（1998- ），女，浙江工商大学硕士生，主要研究方向为知识图谱、新一代网络技术架构" ]
  [ "周静静（1980- ），女，博士，浙江工商大学副教授、硕士生导师，主要研究方向为新一代网络技术架构、软件定义网络、网络流量建模与分析、大数据处理、深度学习等" ]
- 基金信息：
  
  浙江省自然科学基金资助项目;The Zhejiang Province Natural Science Foundation(LY19F020002);浙江省自然科学基金资助项目;The Natural Science Foundation of Zhejiang Province(LY19F020006);浙江省新型网络标准与应用技术重点实验室项目(2013E10012)
- DOI：10.11959/j.issn.1000-0801.2022275
  中图分类号： TP393
- 网络出版日期：2022-11，
  
  纸质出版日期：2022-11-20
- 稿件说明：
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张岳, 张俊楠, 吴晓春, 等. 基于改进灰狼优化算法的服务功能链映射算法[J]. 电信科学, 2022,38(11):57-72.

Yue ZHANG, Junnan ZHANG, Xiaochun WU, et al. Improved grey wolf optimization algorithm based service function chain mapping algorithm[J]. Telecommunications science, 2022, 38(11): 57-72.
张岳, 张俊楠, 吴晓春, 等. 基于改进灰狼优化算法的服务功能链映射算法[J]. 电信科学, 2022,38(11):57-72. DOI： 10.11959/j.issn.1000-0801.2022275.

Yue ZHANG, Junnan ZHANG, Xiaochun WU, et al. Improved grey wolf optimization algorithm based service function chain mapping algorithm[J]. Telecommunications science, 2022, 38(11): 57-72. DOI： 10.11959/j.issn.1000-0801.2022275.

摘要

随着工业互联网、车联网、元宇宙等新型互联网应用的兴起，网络的低时延、可靠性、安全性、确定性等方面的需求正面临严峻挑战。采用网络功能虚拟化技术在虚拟网络部署过程中，存在服务功能链映射效率低与部署资源开销大等问题，联合考虑节点激活成本、实例化开销，以最小化平均部署网络成本为优化目标建立了整数线性规划模型，提出基于改进灰狼优化算法的服务功能链映射（improved grey wolf optimization based service function chain mapping，IMGWO-SFCM）算法。该算法在标准灰狼优化算法基础上添加了基于无环K最短路径（K shortest path，KSP）问题算法的映射方案搜索、映射方案编码以及基于反向学习与非线性收敛改进三大策略，较好地平衡了其全局搜索及局部搜索能力，实现服务功能链映射方案的快速确定。仿真结果显示，该算法在保证更高的服务功能链请求接受率下，相较于对比算法降低了11.86%的平均部署网络成本。

Abstract

With the rise of new Internet applications such as the industrial Internet

the Internet of vehicles

and the metaverse

the network’s requirements for low latency

reliability

security

and certainty are facing severe challenges.In the process of virtual network deployment

when using network function virtualization technology

there were problems such as low service function chain mapping efficiency and high deployment resource overhead.The node activation cost and instantiation cost was jointly considered

an integer linear programming model with the optimization goal of minimizing the average deployment network cost was established

and an improved grey wolf optimization service function chain mapping (IMGWO-SFCM) algorithm was proposed.Three strategies: mapping scheme search based on acyclic KSP algorithm

mapping scheme coding and improvement based on reverse learning and nonlinear convergence were added to the standard grey wolf optimization algorithm to form this algorithm.The global search and local search capabilities were well balanced and the service function chain mapping scheme was quickly determined by IMGWO-SFCM.Compared with the comparison algorithm

IMGWO-SFCM reduces the average deployment network cost by 11.86% while ensuring a higher service function chain request acceptance rate.

关键词

Keywords

references

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