摘要
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.
投稿的翻译标题 | Joint optimization of edge computing and caching in NDN |
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源语言 | 繁体中文 |
页(从-至) | 164-175 |
页数 | 12 |
期刊 | Tongxin Xuebao/Journal on Communications |
卷 | 43 |
期 | 8 |
DOI | |
出版状态 | 已出版 - 25 8月 2022 |
关键词
- cache policy
- deep reinforcement learning
- edge computing
- named data networking