Machine-Learning Gaming Route Selection for Synchronized Play
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Solution Overview
Problem
The synchronization of gameplay for users connected to a game server from different locations is challenging due to varying connection qualities resulting from different routes and changing network performance, leading to inconsistencies in gameplay experience.
Innovation Solution
The use of machine learning models to predict and optimize network connection routes by analyzing historical gaming session data, enabling dynamic route selection and anomaly detection to ensure optimal gameplay performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If different connection routes are used to connect user devices to the game server, then network coverage and accessibility are improved, but connection quality and synchronization consistency deteriorate
Solution Approach 1:
The patent implements dynamic route selection by continuously monitoring network performance metrics (ping, packet loss, jitter) and automatically switching between alternative routes when degradation is detected. The system maintains multiple pre-configured routes and uses real-time data to dynamically determine the optimal path, resolving the contradiction between having multiple routes for coverage and maintaining consistent connection quality.
Solution Approach 2:
The system incorporates feedback mechanisms where network performance data from ongoing game sessions is collected and analyzed to evaluate route quality. This feedback loop enables the system to learn from actual performance and adjust routing decisions, ensuring that the selected routes maintain reliable connection quality while preserving network coverage capabilities.
2Device complexity
If a single connection route is used for simplicity, then system complexity is reduced, but network performance consistency and synchronization reliability deteriorate
Solution Approach 1:
The patent performs preliminary actions by pre-configuring multiple alternative routes and establishing network performance monitoring capabilities before game sessions begin. This preparation allows the system to quickly switch between routes without adding operational complexity during active gameplay, maintaining both simplicity in execution and reliability in performance consistency.
Solution Approach 2:
The routing system operates autonomously by automatically monitoring its own performance metrics and making route selection decisions without external intervention. The system self-adjusts based on real-time network conditions, eliminating the need for complex manual control while ensuring consistent synchronization reliability through automated optimization.
3Reliability
If network performance is monitored continuously to optimize routing, then gameplay synchronization is improved, but processing overhead and system resource consumption increase
Solution Approach 1:
The system implements periodic monitoring of network performance at strategically determined intervals rather than continuous monitoring. By sampling network conditions at appropriate frequencies and only triggering route switches when significant degradation occurs, the system maintains synchronization consistency while minimizing processing overhead and resource consumption associated with constant monitoring.
Data Source
AI summary
Systems and methods for intelligent gaming network traffic routing and optimization are provided. The systems and methods use a combination of different types of machine learning models to predict connection performance between a user device and a game server for various connection routes between the user device and game server. For example, the models may be used to determine which set of nodes (either through a direct connection to the game server or through a gaming private network (GPN)) may provide the most optimal connection at any given time during a game session. The route between the user device and the game server may be selected by the one or more models and may be dynamically updated in real-time throughout the game session as alternative routes are predicted to provide improved network performance.