Gamers Private Network Performance Forecasting. From Raw Data to the Data Warehouse with Machine Learning and Neural Nets

TL;DR

This paper presents a machine learning-based approach to forecast performance changes in Gamers Private Network (GPN) by transforming raw networking data into a structured data warehouse, enabling reliable predictions of network quality for online gaming.

Contribution

It introduces a novel pipeline that converts raw GPN data into a data warehouse and applies neural networks for performance forecasting, enhancing online gaming experience analysis.

Findings

Successful prediction of network performance changes

Quantification of GPN benefits for online gamers

Improved understanding of network dynamics in gaming

Abstract

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.