  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.

Distributed, Parallel, and Cluster Computing;Machine Learning