Autonomous GreenOps: Predictive Orchestration for Zero-Carbon Cloud Computing

The rapid adoption of Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) contributes significantly to Scope 3 carbon emissions in cloud computing. Current mitigation strategies mostly rely on carbon offsetting after the fact, rather than actively managing it during computation. This paper introduces EcoStack, a custom GreenOps middleware platform that restricts heavy AI workloads to times when the power grid generates high levels of renewable energy. By using a Random Forest predictive model trained on UK National Grid ESO data alongside real-time hardware telemetry, EcoStack shifts compute tasks to improve environmental efficiency. We also introduce the Compute-to-Carbon Efficiency (CCE) metric to measure this approach. Initial baseline testing in a localized multi-tenant environment shows the system can execute complex RAG payloads at a grid intensity of 102.0 gCO$_2$/kWh, resulting in a CCE score of 35,294.12 J/gCO$_2$. This setup provides a practical blueprint for reducing the carbon footprint of distributed infrastructure.