An Efficient Load Balancing Scheme for Gaming Server Using Proximal Policy Optimization Algorithm


Hye-Young Kim, Journal of Information Processing Systems Vol. 17, No. 2, pp. 297-305, Apr. 2021  

https://doi.org/10.3745/JIPS.03.0158
Keywords: Dynamic allocation, greedy algorithm, load balancing, proximal policy optimization, Reinforcement Learning
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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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Cite this article
[APA Style]
Kim, H. (2021). An Efficient Load Balancing Scheme for Gaming Server Using Proximal Policy Optimization Algorithm. Journal of Information Processing Systems, 17(2), 297-305. DOI: 10.3745/JIPS.03.0158.

[IEEE Style]
H. Kim, "An Efficient Load Balancing Scheme for Gaming Server Using Proximal Policy Optimization Algorithm," Journal of Information Processing Systems, vol. 17, no. 2, pp. 297-305, 2021. DOI: 10.3745/JIPS.03.0158.

[ACM Style]
Hye-Young Kim. 2021. An Efficient Load Balancing Scheme for Gaming Server Using Proximal Policy Optimization Algorithm. Journal of Information Processing Systems, 17, 2, (2021), 297-305. DOI: 10.3745/JIPS.03.0158.