文献类型：期刊文献

英文题名：An Edge Server Placement Method Based on Reinforcement Learning

作者：Luo, Fei[1];Zheng, Shuai[1];Ding, Weichao[1];Fuentes, Joel[2];Li, Yong[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]Univ Bio Bio, Dept Comp Sci & Informat Technol, Chillan 3780000, Chile

年份：2022

卷号：24

期号：3

外文期刊名：ENTROPY

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000775577200001)】；

基金：This research was funded by the project on Shanghai Science and Technology Innovation Action Plan (No. 20dz1201400, No. 22ZR1416500) and sponsored by Shanghai Sailing Program (20YF1410900).

语种：英文

外文关键词：edge computing; markov decision process; reinforcement learning; access delay; workload balance

摘要：In mobile edge computing systems, the edge server placement problem is mainly tackled as a multi-objective optimization problem and solved with mixed integer programming, heuristic or meta-heuristic algorithms, etc. These methods, however, have profound defect implications such as poor scalability, local optimal solutions, and parameter tuning difficulties. To overcome these defects, we propose a novel edge server placement algorithm based on deep q-network and reinforcement learning, dubbed DQN-ESPA, which can achieve optimal placements without relying on previous placement experience. In DQN-ESPA, the edge server placement problem is modeled as a Markov decision process, which is formalized with the state space, action space and reward function, and it is subsequently solved using a reinforcement learning algorithm. Experimental results using real datasets from Shanghai Telecom show that DQN-ESPA outperforms state-of-the-art algorithms such as simulated annealing placement algorithm (SAPA), Top-K placement algorithm (TKPA), K-Means placement algorithm (KMPA), and random placement algorithm (RPA). In particular, with a comprehensive consideration of access delay and workload balance, DQN-ESPA achieves up to 13.40% and 15.54% better placement performance for 100 and 300 edge servers respectively.