Joint optimization of edge computing and caching in NDN

Yu ZHANG; Min CHENG

doi:10.11959/j.issn.1000-436x.2022160

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Joint optimization of edge computing and caching in NDN

Papers | 更新时间：2024-06-05

- Joint optimization of edge computing and caching in NDN
- Journal on Communications Vol. 43, Issue 8, Pages: 164-175(2022)
- 作者机构：
  
  1. 北京理工大学信息与电子学院，北京 100081
  2. 上海机电工程研究所，上海 201109
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-436x.2022160
  CLC： TP393
- Online First：2022-08，
  
  Published：25 August 2022
- 稿件说明：
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Yu ZHANG, Min CHENG. Joint optimization of edge computing and caching in NDN[J]. Journal on Communications, 2022, 43(8): 164-175.
DOI：

Yu ZHANG, Min CHENG. Joint optimization of edge computing and caching in NDN[J]. Journal on Communications, 2022, 43(8): 164-175. DOI： 10.11959/j.issn.1000-436x.2022160.

摘要

命名数据网络（NDN）基于内容名称进行路由，且节点配备一定的缓存能力，故在架构上更易与边缘计算结合。首先，提出一个在 NDN 中实现网络、计算和缓存动态协调的综合框架。其次，针对不同区域内容流行度的差异性，提出基于矩阵分解的局部内容流行度预测算法；以最大化系统运营收益为目标，利用深度强化学习解决计算和缓存资源分配以及缓存放置策略的联合优化问题。最后，在ndnSIM中构建仿真环境，实验证明所提方案在提高缓存命中率、降低平均时延和远程服务器负载等方面具有明显优势。

Abstract

Named data networking (NDN) is architecturally easier to integrate with edge computing as its routing is based on content names and its nodes have caching capabilities.Firstly

an integrated framework was proposed for implementing dynamic coordination of networking

computing and caching in NDN.Then

considering the variability of content popularity in different regions

a matrix factorization-based algorithm was proposed to predict local content popularity

and deep reinforcement learning was used to solve the the problem of joint optimization for computing and caching resource allocation and cache placement policy with the goal of maximizing system operating profit.Finally

the simulation environment was built in ndnSIM.The simulation results show that the proposed scheme has significant advantages in improving cache hit rate

reducing the average delay and the load on the remote servers.

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references

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