SFC Embedding Meets Machine Learning: Deep Reinforcement Learning Approaches

被引：31

作者：

Liu, Yicen ^{[1
]}

Lu, Yu ^{[1
]}

Li, Xi ^{[1
]}

Qiao, Wenxin ^{[1
]}

Li, Zhiwei ^{[1
]}

Zhao, Donghao ^{[1
]}

机构：

[1] Army Engn Univ Shijiazhuang, Shijiazhuang Campus, Shijiazhuang 050003, Hebei, Peoples R China

来源：

IEEE COMMUNICATIONS LETTERS | 2021年 / 25卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Cloud computing; Internet of Things; Substrates; Servers; Heuristic algorithms; Training; Reinforcement learning; SFC; dynamic embedding; DRL; DDPG; A3C;

D O I：

10.1109/LCOMM.2021.3061991

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

Service function chain (SFC) has been recognized as one of the most important technologies that can satisfy dynamic service demands in the edge clouds. However, how to efficiently embed SFCs in the dynamic edge-cloud scenarios remains as a challenging problem. Considering different network topologies, we devise two deep reinforcement learning (DRL)-based methods for two network sizes: a deep deterministic policy gradient (DDPG) based method for the small-scale networks and an asynchronous advantage actor-critic (A3C) based approach for the large-scale networks. Simulation results demonstrate that our proposals can efficiently deal with the SFC-DMP in edge clouds and outperform the state-of-the-art methods in terms of the delay.

引用

页码：1926 / 1930

页数：5

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