NDN中边缘计算与缓存的联合优化

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

Abstract

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.

Key words: named data networking, edge computing, cache policy, deep reinforcement learning

CLC Number:

TP393

Yu ZHANG, Min CHENG. Joint optimization of edge computing and caching in NDN[J]. Journal on Communications, 2022, 43(8): 164-175.

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References 24

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组合	δ₁	δ₂	δ₃	δ₄	资源分配	平均收益
1	0.25	0.10	0.30	0.10	缓存容量=108，计算资源=127	69.88
2	0.35	0.10	0.15	0.10	缓存容量=136，计算资源=79	67.29
3	0.20	0.10	0.35	0.10	缓存容量=92，计算资源=138	71.13

Joint optimization of edge computing and caching in NDN

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