Geodesic
A Load Balancing Scheme for Gaming Server applying Reinforcement Learning in IoT
Computer Science and Information Systems, Tome 17 (2020) no. 3.

Voir la notice de l'article provenant de la source Computer Science and Information Systems website

A lot of data generated on the game server causes overtime in IoT environment. Recently, both researchers and developers have developed great interests in load balancing schemes in gaming servers. The existing literature have proposed algorithms that distribute loads in servers by mostly concentrating on load balancing and cooperative offloading in Internt of Things (IoT) environment. The dynamic load balancing algorithms have applied a technique of calculating the workload of the network and dynamically allocating the workload according to the network situation, taking into account the capacity of the servers. However, the various previous researches proposed are difficult to reflect the real world by imposing a lot of restrictions and assumptions on the IoT environment, and it is not enough to meet the wide range of service requirements for the IoT environment. Therefore, we proposed an agent that applies a deep reinforced learning method to distribute loads for gaming servers. The agent has accomplished this by measuring network loads and analyzing a large amount of user data. We specifically have chosen deep reinforcement learning because no labels would need to be obtained in advance and it enabled our agent to immediately make the right decisions to load balancing in IoT environment. We have showed several siginicicant functions of our proposed scheme and derived through mathematical analysis. Also, we have compared performances of our proposed scheme and a previus research, ProGreGA, widely used scheme through simulation.
Keywords: deep reinforcement learning, load balancing, gaming server, reward, achievable rate, loss rate, policy 
@article{CSIS_2020_17_3_a13,
     author = {Hye-Young Kim and Jin-Sul Kim},
     title = {A {Load} {Balancing} {Scheme} for {Gaming} {Server} applying {Reinforcement} {Learning} in {IoT}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {17},
     number = {3},
     year = {2020},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2020_17_3_a13/}
}
TY  - JOUR
AU  - Hye-Young Kim
AU  - Jin-Sul Kim
TI  - A Load Balancing Scheme for Gaming Server applying Reinforcement Learning in IoT
JO  - Computer Science and Information Systems
PY  - 2020
VL  - 17
IS  - 3
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/CSIS_2020_17_3_a13/
ID  - CSIS_2020_17_3_a13
ER  - 
%0 Journal Article
%A Hye-Young Kim
%A Jin-Sul Kim
%T A Load Balancing Scheme for Gaming Server applying Reinforcement Learning in IoT
%J Computer Science and Information Systems
%D 2020
%V 17
%N 3
%I mathdoc
%U http://geodesic.mathdoc.fr/item/CSIS_2020_17_3_a13/
%F CSIS_2020_17_3_a13
Hye-Young Kim; Jin-Sul Kim. A Load Balancing Scheme for Gaming Server applying Reinforcement Learning in IoT. Computer Science and Information Systems, Tome 17 (2020) no. 3. http://geodesic.mathdoc.fr/item/CSIS_2020_17_3_a13/