An Efficient Load Balancing Scheme for Gaming Server Using Proximal Policy Optimization Algorithm
- Authors
- Kim, Hye-Young
- Issue Date
- Apr-2021
- Publisher
- KOREA INFORMATION PROCESSING SOC
- Keywords
- Dynamic Allocation; Greedy Algorithm; Load Balancing; Proximal Policy Optimization; Reinforcement Learning
- Citation
- JOURNAL OF INFORMATION PROCESSING SYSTEMS, v.17, no.2, pp.297 - 305
- Journal Title
- JOURNAL OF INFORMATION PROCESSING SYSTEMS
- Volume
- 17
- Number
- 2
- Start Page
- 297
- End Page
- 305
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/15564
- DOI
- 10.3745/JIPS.03.0158
- ISSN
- 1976-913X
- Abstract
- Large amount of data is being generated in gaming servers due to the increase in the number of users and the variety of game services being provided. In particular, load balancing schemes for gaming servers are crucial consideration. The existing literature proposes algorithms that distribute loads in servers by mostly concentrating on load balancing and cooperative offloading. However, many proposed schemes impose heavy restrictions and assumptions, and such a limited service classification method is not enough to satisfy the wide range of service requirements. We propose a load balancing agent that combines the dynamic allocation programming method, a type of greedy algorithm, and proximal policy optimization, a reinforcement learning. Also, we compare performances of our proposed scheme and those of a scheme from previous literature, ProGreGA, by running a simulation.
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- Appears in
Collections - School of Games > Game Software Major > 1. Journal Articles
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