Machine Learning Prediction of Gamer's Private Networks

TL;DR

This paper explores the use of machine learning to predict network latency in Gamer's Private Networks, aiming to optimize online game connectivity and improve user experience.

Contribution

It demonstrates the feasibility of using historical network data for latency prediction in GPNs, paving the way for reinforcement learning-based network optimization.

Findings

Machine learning can effectively predict network latency in GPNs.

Historical data analysis supports future real-time latency prediction.

Potential for reinforcement learning to optimize network configurations.

Abstract

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. Online games are a massive entertainment market and network latency is a key aspect of a player's competitive edge. This market means many different approaches to network architecture are implemented by different competing companies and that those architectures are constantly evolving. Ensuring the optimal connection between a client of WTFast and the online game they wish to play is thus an incredibly difficult problem to automate. 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. 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 the quality of service and reliability of GPN systems.