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http://dx.doi.org/10.3745/JIPS.03.0158

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

Kim, Hye-Young (Dept. of Game Software, School of Games, Hongik University,)
Publication Information
Journal of Information Processing Systems / v.17, no.2, 2021 , pp. 297-305 More about this Journal
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.
Keywords
Dynamic Allocation; Greedy Algorithm; Load Balancing; Proximal Policy Optimization; Reinforcement Learning;
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1 J. Andrews, S. Singh, Q. Ye, X. Lin, and H. Dhillon, "An overview of load balancing in HetNets: old myths and open problems," IEEE Wireless Communications, vol. 21, no. 2, pp. 18-25, 2014.   DOI
2 O. K. Tonguz and E. Yanmaz, "The mathematical theory of dynamic load balancing in cellular networks," IEEE Transactions on Mobile Computing, vol. 7, no. 12, pp. 1504-1518, 2008.   DOI
3 H. Kim, G. de Veciana, X. Yang, and M. Venkatachalam, "Distributed alpha-optimal user association and cell load balancing in wireless networks," IEEE/ACM Transactions on Networking, vol. 20, no. 1, pp. 177-190, 2012   DOI
4 T. He, J. A. Stankovic, C. Lu and T. Abdelzaher, SPEED: a stateless protocol for real-time communication in sensor networks," in Proceedings of IEEE 23rd International conference on Distributed Computing Systems, Providence, RI, 2013, pp. 46-55.
5 H. Y. Kim, H. J. Park, and S. Lee, "A hybrid load balancing scheme for games in wireless networks," International Journal of Distributed Sensor Networks, vol. 10, no. 5, article no. 380318, 2014. https://doi.org/10.1155%2F2014%2F380318   DOI
6 S. T. Cheng and T. Y. Chang, "An adaptive learning scheme for load balancing with zone partition in multi-sink wireless sensor network," Expert Systems with Applications, vol. 39, no. 10, pp. 9427-9434, 2012.   DOI
7 R. Kacimi, R. Dhaou and A. L. Beylot, "Load balancing techniques for lifetime maximizing in wireless sensor networks," Ad Hoc Networks, vol. 11, pp. 2172-2186, 2013.   DOI
8 W. H. Liao, K. P. Shih, and W. C. Wu, "A grid-based dynamic load balancing approach for data-centric storage in wireless sensor networks," Computers & Electrical Engineering, vol. 36, no. 1, pp. 19-30, 2010.   DOI
9 V. Nae, A. Iosup, and R. Prodan, "Dynamic resource provisioning in massively multiplayer online games," IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 3, pp. 380-395, 2011.   DOI
10 R. E. De Grande and A. Boukerche, "Dynamic partitioning of distributed virtual simulations for reducing communication load," in Proceedings of 2009 IEEE International Workshop on Haptic Audio visual Environments and Games, Lecco, Italy, 2009, pp. 176-181.
11 X. Li, Y. J. Kim, R. Govindan, and W. Hong, "Multi-dimensional range queries in sensor networks," in Proceedings of the 1st International Conference on Embedded Networked Sensor Systems, Los Angeles, CA, 2003, pp. 63-75.
12 R. Tlili and Y. Slimani, "A hierarchical dynamic load balancing strategy for distributed data mining," International Journal of Advanced Science and Technology, vol. 39, pp. 21-48, 2012.
13 J. N. Foerster, Y. M. Assael, N. De Freitas, and S. Whiteson, "Learning to communicate with deep multi-agent reinforcement learning," Advances in Neural Information Processing Systems, vol. 29, pp. 2137-2145, 2016.
14 A. Souri and R. Hosseini, "A state-of-the-art survey of malware detection approaches using data mining techniques," Human-centric Computing and Information Sciences, vol. 8, article no. 3, 2018. https://doi.org/10.1186/s13673-018-0125-x   DOI
15 J. Wang, X. Gu, W. Liu, A. K. Sangaiah, and H. J. Kim, "An empower Hamilton loop based data collection algorithm with mobile agent for WSNs," Human-centric Computing and Information Sciences, vol. 9, article no. 18, 2019. https://doi.org/10.1186/s13673-019-0179-4   DOI
16 J. Li, G. Luo, N. Cheng, Q. Yuan, Z. Wu, S. Gao, and Z. Liu, "An end-to-end load balancer based on deep learning for vehicular network traffic control," IEEE Internet of Things Journal, vol. 6, no. 1, pp. 953-966, 2019.   DOI
17 M. C. Fu, "Gradient estimation," Handbooks in Operations Research and Management Science, vol. 13, pp. 575-616, 2006.
18 L. Chen, J. Lingys, K. Chen, and F. Liu, "Auto: scaling deep reinforcement learning for datacenter-scale automatic traffic optimization," in Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication, Budapest, Hungary, 2018, pp. 191-205.
19 I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA: MIT Press, 2016.
20 J. M. Park, H. Y. Kim, and S. H. Cho, "A study on load distribution of gaming server using proximal policy optimization," Journal of Korea Game Society, vol. 19, no. 3, pp. 5-14, 2019.   DOI
21 P. Quax, J. Cleuren, W. Vanmontfort, and W. Lamotte, "Empirical evaluation of the efficiency of spatial subdivision schemes and load balancing strategies for networked games," in Proceedings of 2011 Proceedings of 20th International Conference on Computer Communications and Networks (ICCCN), Lahaina, HI, 2011, pp. 1-6.
22 C. Yin, B. Zhou, Z. Yin, and J. Wang, "Local privacy protection classification based on human-centric computing," Human-centric Computing and Information Sciences, vol. 9, article no. 33, 2019. https://doi.org/10.1186/s13673-019-0195-4   DOI
23 Z. Zhang, L. Ma, K. K. Leung, L. Tassiulas, and J. Tucker, "Q-placement: reinforcement-Learning-based service placement in software defined networks," in Proceedings of 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), Vienna, Austria, 2018, pp. 1527-1532.