SDN-based MEC resource allocation of a power grid

被引：0
|
作者：
Shang L. [1 ]
Cai S. [1 ]
Cui J. [1 ]
Ji C. [1 ]
Cui K. [2 ]
Li B. [2 ]
机构：
[1] State Grid Hebei Electric Power Co., Ltd. Information and Communication Branch, Shijiazhuang
[2] School of Electrical & Electronic Engineering, North China Electric Power University, Baoding
来源：
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2021年 / 49卷 / 20期
基金：
中国国家自然科学基金;
关键词：
Mobile edge computing; Reinforcement learning; Resource allocation; Resource virtualization; Software defined network;
D O I：
10.19783/j.cnki.pspc.201623
中图分类号：
学科分类号：
摘要：
To solve the problem of limited resources of edge servers in grid nodes, an edge computing framework based on a software-defined network is proposed. The Depth Deterministic Strategy Gradient (DDPG) reinforcement learning algorithm is used to allocate the computing and storage resources reasonably in edge servers. First, an edge computing model applied into the software-defined grid network is established. This is to obtain the edge node computing and cache resources and task delay constraints, in order to analyze the MINIP which needs to be solved. TensorFlow is used to build a simulation environment and execute the reinforcement learning algorithm to realize the optimal utilization of edge server storage computing resources by grid edge nodes. It shows that the rewards of reinforcement learning increase, and the total delay of the system using DDPG allocation is lower. © 2021 Power System Protection and Control Press.
引用
收藏
页码：136 / 143
页数：7
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