MEC中基于改进遗传模拟退火算法的虚拟网络功能部署策略

陈卓; 冯钢; 刘怡静; 周杨

doi:10.11959/j.issn.1000-436x.2020074

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MEC中基于改进遗传模拟退火算法的虚拟网络功能部署策略

学术论文 | 更新时间：2024-06-05

- MEC中基于改进遗传模拟退火算法的虚拟网络功能部署策略
- Virtual network function deployment strategy based on improved genetic simulated annealing algorithm in MEC
- 通信学报 2020年41卷第4期页码：70-80
- 作者机构：
  
  1. 重庆理工大学计算机科学与工程学院，重庆 200433
  2. 电子科技大学通信抗干扰技术国家级重点实验室，四川成都 710077
  3. 奥本大学计算机科学与软件工程学院，奥本 36849
- 作者简介：
  
  [ "陈卓（1980- ），男，重庆人，博士，重庆理工大学副教授、硕士生导师，主要研究方向为网络虚拟化、物联网应用及网络性能分析和评估" ]
  [ "冯钢（1964- ），男，重庆人，博士，电子科技大学教授、博士生导师，主要研究方向为无线通信网络、网络虚拟化及网络资源分配" ]
  [ "刘怡静（1994- ），女，四川成都人，电子科技大学硕士生，主要研究方向为无线电通信网络、网络虚拟化" ]
  [ "周杨（1975- ），男，重庆人，博士，奥本大学助理教授、博士生导师，主要研究方向为最优算法、大数据技术" ]
- 基金信息：
  
  国家自然科学基金资助项目(61471089);国家自然科学基金资助项目(61401076);重庆市技术创新与应用发展基金资助项目(cstc2018jszx-cyztzx0088)
- DOI：10.11959/j.issn.1000-436x.2020074
  中图分类号： TP393.0
- 网络首发：2020-04，
  
  纸质出版：2020-04-25
- 稿件说明：
移动端阅览
陈卓, 冯钢, 刘怡静, 等. MEC中基于改进遗传模拟退火算法的虚拟网络功能部署策略[J]. 通信学报, 2020,41(4):70-80.

Zhuo CHEN, Gang FENG, Yijing LIU, et al. Virtual network function deployment strategy based on improved genetic simulated annealing algorithm in MEC[J]. Journal on Communications, 2020, 41(4): 70-80.
陈卓, 冯钢, 刘怡静, 等. MEC中基于改进遗传模拟退火算法的虚拟网络功能部署策略[J]. 通信学报, 2020,41(4):70-80. DOI： 10.11959/j.issn.1000-436x.2020074.

Zhuo CHEN, Gang FENG, Yijing LIU, et al. Virtual network function deployment strategy based on improved genetic simulated annealing algorithm in MEC[J]. Journal on Communications, 2020, 41(4): 70-80. DOI： 10.11959/j.issn.1000-436x.2020074.

摘要

为了有效改善多集群共存的移动边缘网络中业务流端到端服务时延，提出了一种基于改进遗传模拟退火算法的虚拟网络功能部署策略。通过开放 Jackson 排队网络对移动业务流的时延进行最优化建模，在证明其 NP性的基础上提出了将遗传算法与模拟退火算法相结合的求解策略，该策略通过对服务节点的提前映射机制避免了可能带来的网络拥塞，并通过个体的约束性判断和纠正遗传的方法避免了局部最优的出现。在不同的服务请求量、服务节点规模、集群数量及虚拟网络功能之间的逻辑连接关系等参数下的对比实验表明，该策略能提供更低时延的端到端服务，使时延敏感类移动业务获得更好体验。

Abstract

In order to effectively improve the end-to-end service delay of the flow in multi-clusters coexisting mobile edge computing (MEC) network

a virtual network function deployment strategy based on improved genetic simulated annealing algorithm was proposed.The delay of mobile service flow was mathematically modeled through the open Jackson queuing network.After proving the NP attribute of this problem

a solution combining genetic algorithm and simulated annealing algorithm was proposed.In this strategy

the advance mapping mechanism avoids the possibility of network congestion

and the occurrence of local optima was avoided through using the methods of individual judgment and corrective genetic.Extensive simulation was set up to evaluate the effectiveness of the proposed strategy under different parameter settings

such as different volume of requests

different scale of service nodes

different number of MEC clusters

and logical link relationships between virtual network functions.Results show that this strategy can provide lower end-to-end services delay and better service experience for latency-sensitive mobile application.

关键词

Keywords

references

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