Auto-aware Dynamic Control Policy for Network Energy Efficiency

The system performs preliminary actions by continuously collecting and analyzing network traffic data to train machine learning models in advance. These models predict future traffic patterns before they occur, enabling the energy efficiency controller to proactively adjust network parameters and device states ahead of actual traffic changes, thus achieving real-time responsiveness without complex on-the-fly analysis

The patent introduces machine learning models as intermediary components between raw historical traffic data and energy efficiency control decisions. These models act as mediators that process historical data and generate predictive insights, which then guide the control policy. This intermediary layer enables accurate real-time predictions while keeping the control implementation relatively simple and modular

The patent merges link-specific control policies with network-wide traffic predictions by integrating local energy optimization decisions with global network context. The system combines measurements from multiple links and uses centralized machine learning models to coordinate control actions across the network, ensuring that local power savings contribute to overall network energy efficiency rather than operating in isolation

The control policy framework is designed to be universal and multi-functional, serving both local link optimization and global network energy efficiency goals. The same machine learning-based prediction engine and control architecture are applied across multiple links and network nodes, enabling the system to adapt to different traffic patterns and optimization requirements while maintaining a unified approach to energy management

The patent implements dynamic control policies that automatically adapt to changing traffic patterns through machine learning models. These models continuously learn from historical data and adjust predictions based on observed traffic variations, enabling the system to dynamically optimize energy efficiency without requiring manual reconfiguration or complex rule-based logic. The dynamics are built into the learning algorithm itself

The system employs self-service mechanisms where the machine learning models automatically train on collected traffic data and generate control decisions without external intervention. The control policy serves itself by using its own performance data to improve future predictions and adjustments, reducing the need for complex external control logic and manual tuning while maintaining high energy efficiency