Machine Learning Prediction of Gamer's Private Networks (GPN~?S)

摘要

The Gamer's Private Network (GPN~?) is a client/server technology created by WTFast for making the network performance of online games faster and more reliable. GPN~?s use middle-mile servers and proprietary algorithms to better connect online video-game players to their game's servers across a wide-area network. Using machine learning, we analyzed historical network data from GPN~? connections to explore the feasibility of network latency prediction which is a key part of optimization. The prediction models are encouraging and provide useful knowledge about the statistics of GPN routing. Our next step will be to collect live data (including client/server load, packet and port information and specific game state information) from GPN~? Minecraft servers and bots. We will use this information in a Reinforcement Learning model along with predictions about latency to alter the clients' and servers' configurations for optimal network performance. These investigations and experiments will improve quality of service and reliability for GPN~? systems.