A Reinforcement Learning based Edge Cloud Collaboration

被引：1
|
作者：
Kobari, Hiroki [1 ]
Du, Zhaoyang [1 ]
Wu, Celimuge [1 ]
Yoshinaga, Tsutomu [1 ]
Bao, Wugedele [2 ]
机构：
[1] Univ Electrocommun, Tokyo, Japan
[2] Hohhot Minzu Coll, Hohhot, Peoples R China
来源：
2021 INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES FOR DISASTER MANAGEMENT (ICT-DM) | 2021年
基金：
中国国家自然科学基金;
关键词：
Cloud computing; Edge computing; Q-learning;
D O I：
10.1109/ICT-DM52643.2021.9664025
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Recently, edge computing has attracted more and more attention. Compared with traditional cloud computing, edge computing can reduce communication delay. However, the processing capability of edge computing is not as good as cloud computing. The proposed method combines the advantage of the low communication delay of edge computing and the high processing capability of cloud computing. We use the Q-learning algorithm to balance network load between the edge server and the cloud server to reduce the average service time. Simulation results show that the proposed method suppresses the task failure rate while reducing the average service time.
引用
收藏
页码：26 / 29
页数：4
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