MUMBAI, India, Oct. 5 -- Intellectual Property India has published a patent application (202631099528 A) filed by Mr Aditya Rautaray on August 18, 2026, for An Intelligent Energy Optimization Framework For Cloud Data Centers Using Machine Learning.
Inventor includes Mr Aditya Rautaray.
The application for the patent was published on October 02, 2026, under issue no. 40/2026.
Abstract: An Intelligent Energy Optimization Framework for Cloud Data Centers Using Machine Learning ABSTRACT The present invention relates to intelligent energy optimization framework for a cloud data center based on Machine Learning. The framework encompasses real-time monitoring, predictive analytics, resource orchestration and automated control, to minimize energy usage and keep applications performing, reliable, thermally safe and service level requirements intact. They gather and process operational information on processor usage, memory usage, storage activity, network traffic, energy consumption of the server, temperature, cooling performance, electricity price, renewable energy availability and carbon intensity. Machine-learning models predict workloads and energy demand, uncover inefficient resources, spot anomalies and forecast thermal conditions. An optimization engine analyzes the predictions and makes decisions on what actions to take, such as workload scheduling, migration of virtual machines, relocation of containers, consolidation of servers, selective activation and deactivation of computing resources, and adaptive cooling control. The framework can also redistribute build-out loads from one time period to another, or from a geographically distributed data center to another data center, based on cost, latency, capacity, renewable energy availability, and regulatory considerations. Feedback mechanism: A process of comparing the predicted results with the actual results, and modifying the models accordingly to make better forecasts in the future. The following features avert harmful actions: Safety policies, confidence thresholds, rollback functions, service-level monitoring. The revealed framework thus allows for scalable, automated, economic and environmentally friendly management of the energy consumption of cloud data-center. It is designed for edge, hyperscale, public, private, and hybrid cloud deployments anywhere around the world.
Disclaimer: Curated by HT Syndication.
