ML-Based TCP Rate Control for Predictive Congestion Avoidance

The patent applies preliminary action by using machine learning algorithms to predict congestion before it occurs. The system analyzes historical traffic patterns and network conditions to forecast potential congestion events, allowing the transmission rate to be adjusted proactively rather than reactively. This prevents the slow-start mechanism from being triggered in the first place, maintaining higher transmission rates while avoiding actual congestion.

The patent implements feedback by continuously monitoring network conditions and using machine learning models to analyze traffic patterns. The system receives feedback about actual congestion events and uses this information to refine its predictions and adjust transmission rates dynamically. This closed-loop control enables the system to learn from past congestion events and improve its predictive accuracy over time.

The patent uses machine learning algorithms as an intermediary between raw network traffic data and transmission rate control decisions. Instead of directly controlling transmission based on simple congestion signals, the ML algorithm processes complex traffic patterns and provides refined predictions that guide rate adjustment. This intermediary layer translates complex network behavior into actionable insights while managing the complexity through standardized algorithmic approaches.

The system applies self-service by implementing machine learning models that automatically adapt to changing network conditions without requiring manual configuration or intervention. The algorithms learn from historical data and continuously refine their predictions, enabling the system to serve itself by automatically optimizing transmission rates based on learned patterns rather than predefined rules.