NDN中边缘计算与缓存的联合优化

张宇; 程旻

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

您当前的位置：

首页 >

文章列表页 >

NDN中边缘计算与缓存的联合优化

学术论文 | 更新时间：2024-06-05

- NDN中边缘计算与缓存的联合优化
- Joint optimization of edge computing and caching in NDN
- 通信学报 2022年43卷第8期页码：164-175
- 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：
  
  [ "张宇（1972- ），男，山西原平人，博士，北京理工大学讲师，主要研究方向为网络的路由和缓存等资源分配优化、网络体系架构、协议实现和NS-3仿真等" ]
  [ "程旻（1997- ），女，安徽黄山人，北京理工大学硕士生，主要研究方向为命名数据网络、边缘计算和网络资源优化等" ]
- 基金信息：
  
  国家重点研发计划基金资助项目;The National key Research and Development Program of china(2019YFB1803200)
- DOI：10.11959/j.issn.1000-436x.2022160
  中图分类号： TP393
- 网络首发：2022-08，
  
  纸质出版：2022-08-25
- 稿件说明：
移动端阅览
张宇, 程旻. NDN中边缘计算与缓存的联合优化[J]. 通信学报, 2022,43(8):164-175.

Yu ZHANG, Min CHENG. Joint optimization of edge computing and caching in NDN[J]. Journal on Communications, 2022, 43(8): 164-175.
张宇, 程旻. NDN中边缘计算与缓存的联合优化[J]. 通信学报, 2022,43(8):164-175. DOI： 10.11959/j.issn.1000-436x.2022160.

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.

关键词

Keywords

references

ZHANG L X , AFANASYEV A , BURKE J , et al . Named data networking [J ] . ACM SIGCOMM Computer Communication Review , 2014 , 44 ( 3 ): 66 - 73 .

JAYAKUMAR S , SHEELVANTHMATH P , AKKI C B . Technical analysis of content placement algorithms for content delivery network in cloud [J ] . International Journal of Electrical and Computer Engineering (IJECE) , 2022 , 12 ( 1 ): 489 .

ZULFA M I , HARTANTO R , PERMANASARI A E . Caching strategy for Web application—a systematic literature review [J ] . International Journal of Web Information Systems , 2020 , 16 ( 5 ): 545 - 569 .

CHEN M , HAO Y X , HU L , et al . Edge-CoCaCo:toward joint optimization of computation,caching,and communication on edge cloud [J ] . IEEE Wireless Communications , 2018 , 25 ( 3 ): 21 - 27 .

JIANG C F , CHENG X L , GAO H H , et al . Toward computation offloading in edge computing:a survey [J ] . IEEE Access , 2019 , 7 : 131543 - 131558 .

XU J , CHEN L X , ZHOU P . Joint service caching and task offloading for mobile edge computing in dense networks [C ] // Proceedings of IEEE INFOCOM 2018 - IEEE Conference on Computer Communications . Piscataway:IEEE Press , 2018 : 207 - 215 .

POLYAK B , SHCHERBAKOV P . Lyapunov functions:an optimization theory perspective [J ] . IFAC-PapersOnLine , 2017 , 50 ( 1 ): 7456 - 7461 .

MNIH V , KAVUKCUOGLU K , SILVER D , et al . Playing Atari with deep reinforcement learning [J ] . arXiv Preprint,arXiv:1312.5602 , 2013 .

MNIH V , KAVUKCUOGLU K , SILVER D , et al . Human-level control through deep reinforcement learning [J ] . Nature , 2015 , 518 ( 7540 ): 529 - 533 .

CHEN X T , GE H B , LIU L H , et al . Computing offloading decision based on DDPG algorithm in mobile edge computing [C ] // Proceedings of 2021 IEEE 6th International Conference on Cloud Computing and Big Data Analytics . Piscataway:IEEE Press , 2021 : 391 - 399 .

KOREN Y , BELL R , VOLINSKY C . Matrix factorization techniques for recommender systems [J ] . Computer , 2009 , 42 ( 8 ): 30 - 37 .

LILLICRAP T P , HUNT J J , PRITZEL A , et al . Continuous control with deep reinforcement learning [J ] . arXiv Preprint,arXiv:1509.02971 , 2015 .

ZHU H , CAO Y , WANG W , et al . Deep reinforcement learning for mobile edge caching:review,new features,and open issues [J ] . IEEE Network , 2018 , 32 ( 6 ): 50 - 57 .

ZHU G X , LIU D Z , DU Y Q , et al . Toward an intelligent edge:wireless communication meets machine learning [J ] . IEEE Communications Magazine , 2020 , 58 ( 1 ): 19 - 25 .

LAOUTARIS N , CHE H , STAVRAKAKIS I . The LCD interconnection of LRU caches and its analysis [J ] . Performance Evaluation , 2006 , 63 ( 7 ): 609 - 634 .

CHEN M , LI W , FORTINO G , et al . A dynamic service migration mechanism in edge cognitive computing [J ] . ACM Transactions on Internet Technology , 2019 , 19 ( 2 ): 1 - 15 .

ZHANG C , ZHENG Z X . Task migration for mobile edge computing using deep reinforcement learning [J ] . Future Generation Computer Systems , 2019 , 96 : 111 - 118 .

HE Y , YU F R , ZHAO N , et al . Software-defined networks with mobile edge computing and caching for smart cities:a big data deep reinforcement learning approach [J ] . IEEE Communications Magazine , 2017 , 55 ( 12 ): 31 - 37 .

KADER M A , BASTUG E , BENNIS M , et al . Leveraging big data analytics for cache-enabled wireless networks [C ] // Proceedings of 2015 IEEE Globecom Workshops . Piscataway:IEEE Press , 2015 : 1 - 6 .

YU Z X , HU J , MIN G Y , et al . Proactive content caching for Internet-of-vehicles based on peer-to-peer federated learning [C ] // Proceed ings of 2020 IEEE 26th International Conference on Parallel and Distributed Systems . Piscataway:IEEE Press , 2020 : 601 - 608 .

KAIROUZ E B P , MCMAHAN H B . Advances and open problems in federated learning [J ] . arXiv Preprint,arXiv:1912.04977 , 2019 .

BANNOUR F , SOUIHI S , MELLOUK A . Distributed SDN control:survey,taxonomy,and challenges [J ] . IEEE Communications Surveys＆ Tutorials , 2018 , 20 ( 1 ): 333 - 354 .

DING Z Y , LI W , CHENG Y F , et al . Slice network framework and use cases based on FlexE technology for power services [C ] // Proceedings of 2021 International Wireless Communications and Mobile Computing (IWCMC) . Piscataway:IEEE Press , 2021 : 57 - 62 .

浏览量

1179

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于深度强化学习的海洋移动边缘计算卸载方法

基于DDPG强化学习的多集群算力资源调度算法

智能反射表面辅助的车载边缘任务卸载和资源分配策略

数字孪生辅助下基于D3QN的车载网络协同卸载算法

基于深度强化学习的分离式数据中心存储资源调度优化方法