Network Power Prediction via Simulation and Machine Learning

The patent applies preliminary action by using simulation to generate synthetic network power usage data before actual deployment. The simulation environment pre-generates training datasets that mimic real-world conditions, allowing the machine learning model to be trained in advance without requiring extensive real-world data collection. This resolves the contradiction by obtaining sufficient training data through simulation beforehand, thereby maintaining prediction accuracy while dramatically reducing the time needed for actual data collection.

The patent applies copying by creating a virtual copy of the network infrastructure through simulation. Instead of collecting data from physical hardware, the system creates a digital twin that replicates network behavior and power consumption patterns. This virtual copy generates synthetic training data that is sufficiently accurate for model training, thereby achieving good prediction accuracy without the time-consuming process of collecting data from actual deployed systems.

The patent uses copying by creating a virtual simulation environment that replicates network infrastructure behavior. This digital twin generates synthetic training data without requiring physical instrumentation of actual network devices. The simulation approach maintains model accuracy by faithfully reproducing power consumption patterns while significantly reducing system complexity compared to deploying sensors and data collection infrastructure across physical network equipment.

The patent introduces simulation as an intermediary between the physical network infrastructure and the machine learning model. Rather than directly collecting data from complex physical systems, the simulation layer acts as a mediator that translates physical network behavior into usable training data. This intermediary approach maintains model accuracy while simplifying the overall system architecture by replacing complex data collection infrastructure with a software-based simulation layer.

The patent applies preliminary action by performing comprehensive simulation-based data generation and model training before actual deployment. The simulation environment is configured to replicate diverse network conditions and workloads, generating sufficient training data in advance to achieve accurate predictions. By completing the data collection and model training process preliminarily through simulation, the system achieves both high productivity during deployment and maintains prediction accuracy through thorough pre-training.

The patent uses copying by creating a faithful virtual representation of the network infrastructure that accurately replicates power consumption characteristics. The simulation model is designed to copy real-world network behavior patterns, ensuring that synthetic training data reflects actual operational conditions. This accurate copying maintains prediction accuracy while enabling rapid data generation without physical hardware constraints.

The patent applies feedback by implementing a predictive power management system that uses machine learning models to forecast network power consumption. The system continuously monitors actual power usage, compares it with predictions, and uses this feedback to refine the model and improve future predictions. This feedback mechanism enables dynamic power allocation that adapts to fluctuating consumption patterns, significantly improving power allocation efficiency while maintaining reasonable implementation complexity through automated model updates.

The patent applies dynamics by transitioning from static power management to dynamic predictive power management. The machine learning model continuously adapts to changing network conditions and workloads, providing real-time power consumption predictions that enable flexible power allocation. This dynamic approach improves power allocation efficiency by responding to actual usage patterns while maintaining implementation feasibility through automated model training and updating processes.