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

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.

💡 Research Summary

The paper presents a comprehensive end‑to‑end framework for forecasting the performance of a Gamers Private Network (GPN) service, using massive raw networking logs collected by the WTFast platform. First, the authors describe the data acquisition pipeline: real‑time streaming of per‑second metrics such as round‑trip time (RTT), packet loss, throughput, routing path, ISP, geographic location, game title, and session timestamps. These logs are ingested via Apache Kafka, buffered, and processed with Apache Flink to handle missing values, outlier removal, time‑synchronization, and normalization. The cleaned data are stored in columnar Parquet files on AWS S3 and subsequently loaded into an AWS Redshift‑based data warehouse designed with a hybrid star‑and‑snowflake schema, separating fact tables (session‑level performance) from dimension tables (users, games, regions, ISPs).

Business intelligence dashboards are built with Tableau and PowerBI for real‑time KPI monitoring. For predictive analytics, the authors first apply classical time‑series models (Prophet, ARIMA, SARIMA) but find them insufficient for capturing nonlinear interactions. Consequently, they develop deep learning models based on LSTM and GRU networks, training on over 100 million labeled sessions (80 % train, 10 % validation, 10 % test). Hyper‑parameter tuning via Bayesian Optimization yields a final LSTM model that reduces mean squared error by more than 12 % compared with the best ARIMA baseline, achieving R² = 0.87, MAE ≈ 4.3 ms, and RMSE ≈ 6.1 ms across 5‑minute, 30‑minute, and 1‑hour forecasting horizons.

Interpretability is addressed using SHAP values, revealing that ISP, server‑client distance, regional congestion, and game type are the dominant drivers of latency. Quantitative results show that GPN usage lowers average RTT from 45 ms to 28 ms (a 38 % reduction), cuts packet loss from 0.8 % to 0.3 % (62 % reduction), and reduces latency variance during peak hours by 45 % relative to standard internet connections. These improvements translate into a service‑level agreement (SLA) compliance rate exceeding 99.9 % and provide a data‑driven basis for proactive network routing adjustments, market expansion planning, and targeted marketing ROI calculations. In sum, the study demonstrates how a robust ETL pipeline, a purpose‑built data warehouse, and advanced machine‑learning techniques can together deliver accurate performance forecasts and measurable business value for large‑scale, latency‑sensitive gaming networks.