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http://dx.doi.org/10.7583/JKGS.2019.19.3.5

A Study on Load Distribution of Gaming Server Using Proximal Policy Optimization  

Park, Jung-min (Game Department, The Graduate School)
Kim, Hye-young (School of Games, Hongik University)
Cho, Sung Hyun (School of Games, Hongik University)
Publication Information
Journal of Korea Game Society / v.19, no.3, 2019 , pp. 5-14 More about this Journal
Abstract
The gaming server is based on a distributed server. In order to distribute workloads of gaming servers, distributed gaming servers apply some algorithms which divide each of gaming server's workload into balanced workload among the gaming servers and as a result, efficiently manage response time and fusibility of server requested by the clients. In this paper, we propose a load balancing agent using PPO(Proximal Policy Optimization) which is one of the methods from a greedy algorithm and Policy Gradient which is from Reinforcement Learning. The proposed load balancing agent is compared with the previous researches based on the simulation.
Keywords
Load balance; Game server; Reinforcement Learning;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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