Gamers Private Network Performance Forecasting. From Raw Data to the Data Warehouse with Machine Learning and Neural Nets
This work addresses improving online gaming experience through reliable, low-latency connections for gamers, but appears incremental as it applies existing methods to new data.
The study tackled predicting network performance for Gamers Private Network (GPN) by transforming raw data into a data warehouse and using machine learning and neural nets, demonstrating the ability to forecast network changes and quantify user benefits from GPN usage.
Gamers Private Network (GPN) is a client/server technology that guarantees a connection for online video games that is more reliable and lower latency than a standard internet connection. Users of the GPN technology benefit from a stable and high-quality gaming experience for online games, which are hosted and played across the world. After transforming a massive volume of raw networking data collected by WTFast, we have structured the cleaned data into a special-purpose data warehouse and completed the extensive analysis using machine learning and neural nets technologies, and business intelligence tools. These analyses demonstrate the ability to predict and quantify changes in the network and demonstrate the benefits gained from the use of a GPN for users when connected to an online game session.