卫星CDN中基于DQN的资源编排算法

张嘉然; 杨雅婷; 嵩天

doi:10.11959/j.issn.2096-8930.2022042

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卫星CDN中基于DQN的资源编排算法

专题：卫星互联网用户接入控制 | 更新时间：2024-06-03

- 卫星CDN中基于DQN的资源编排算法
- Resource Scheduling Algorithm Based on DQN in Satellite CDN
- 天地一体化信息网络 2022年3卷第4期页码：45-54
- 作者机构：
  
  1. 北京理工大学计算机学院，北京 100081
  2. 北京理工大学网络空间安全学院，北京 100081
- 作者简介：
  
  [ "张嘉然（1999-），北京理工大学计算机学院硕士生，主要研究方向为卫星互联网、内容分发网络" ]
  [ "杨雅婷（1994-），北京理工大学网络空间安全学院助理教授、特别副研究员，硕士生导师，主要研究方向为卫星互联网、信息中心网络" ]
  [ "嵩天（1980-），北京理工大学网络空间安全学院副院长、教授，博士生导师，主要研究方向为卫星互联网、信息中心网络、高速分组处理和网络安全" ]
- 基金信息：
  
  国家重点研发计划资助项目;National Key Research and Development Program of China(2020YFB1806000)
- DOI：10.11959/j.issn.2096-8930.2022042
  中图分类号： TN393
- 网络出版日期：2022-12，
  
  纸质出版日期：2022-12-20
- 稿件说明：
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张嘉然, 杨雅婷, 嵩天. 卫星CDN中基于DQN的资源编排算法[J]. 天地一体化信息网络, 2022,3(4):45-54.

Jiaran ZHANG, Yating YANG, Tian SONG. Resource Scheduling Algorithm Based on DQN in Satellite CDN[J]. Space-integrated-ground information networks, 2022, 3(4): 45-54.
张嘉然, 杨雅婷, 嵩天. 卫星CDN中基于DQN的资源编排算法[J]. 天地一体化信息网络, 2022,3(4):45-54. DOI： 10.11959/j.issn.2096-8930.2022042.

Jiaran ZHANG, Yating YANG, Tian SONG. Resource Scheduling Algorithm Based on DQN in Satellite CDN[J]. Space-integrated-ground information networks, 2022, 3(4): 45-54. DOI： 10.11959/j.issn.2096-8930.2022042.

摘要

随着空间与信息技术迅速发展，热点内容分发密集型场景将成为卫星网络应用的重点方向，而卫星内容分发网络（CDN）是提高空天内容分发效率的重要手段。针对卫星CDN体系架构中存在的业务需求时空不均、卫星资源稀缺、现有编排算法适应性不足等问题，提出一种基于深度Q学习（Deep Q-Learning

DQN）的资源编排算法。该方法首先对用户请求进行分类，依据卫星时变运行轨迹和星地资源情况，计算出卫星可通信的最短路径集合；之后通过马尔可夫模型建模量化卫星和用户的相关信息，利用DQN算法计算出最优的卫星CDN存储节点，达到降低用户请求时延，降低星地资源占用率，提高缓存命中率的效果。

Abstract

With the rapid development of space and information fi eld

hot content distribution intensive scenes will become one of the key directions of satellite network application

and satellite content delivery network (CDN) network is an important means to improve the effi ciency of air and space content distribution.In the architecture of satellite CDN network

due to the uneven time and space of business requirements

the scarcity of satellite resources and the insuffi cient adaptability of existing scheduling algorithms

scheduling algorithms for satellite resources are faced with problems such as high resource dimension

many computing states and large amount of computation

which will reduce the accuracy

response speed and computing performance of scheduling decisions.To solve this problem

a resource scheduling algorithm based on Deep Q-Learning (DQN) algorithm was proposed to improved the effi ciency and accuracy of satellite resource scheduling

and intelligently and quickly perceived the resource situation to make scheduling decisions.Firstly

the user requests were classifi ed

and the shortest path set that the satellite could communicated with was calculated according to the time-varying trajectory of the satellite and the resources of the satellite and the ground.After that

the related information of satellites and users was quantifi ed by Markov model modeling

and the optimal CDN storage node of satellites was calculated by DQN algorithm

which achieved the eff ects of reduced user request delay

reduced satellite-ground resource occupancy rate and improved cache hit rate.

关键词

Keywords

references

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