EasySM: A Data-Driven Intelligent Decision Support System for Server Merge
Keywords:Data Mining, Time Series Prediction, Decision Support System, Online Game
AbstractAs an independent social and economic entity, game servers plays a dominant role in building a stable, living, and attractive virtual world in massive multi-player online role-playing games (MMORPGs). We propose and implement a novel intelligent decision support system for server merge (SM) for maintaining the game ecology at the macro level. The services provided by this system include server health diagnosis, server merge assessment, and combination strategy recommendation. Specifically, we design an effective time series prediction algorithm to diagnose the health status of one server (e.g., user activity, online time, daily revenue) based on real game scenarios, and then select the servers with poor status from all servers. Moreover, to dig out the inherent development laws of servers from the historical merge records, we leverage a correlation measurement algorithm to find the historical merged servers that are similar to the servers to be merged and then evaluate the potential trend after merging, which can assist experts to make reasonable decisions. We deploy our system into practice for multiple MMORPGs and achieve sound online performance endorsed by the game operation team.
How to Cite
Qu, M., Huang, J., Deng, H., Wu, R., Shen, X., Tao, J., & Lv, T. (2022). EasySM: A Data-Driven Intelligent Decision Support System for Server Merge. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13212-13214. https://doi.org/10.1609/aaai.v36i11.21731
AAAI Demonstration Track