Carbonbalance framework for carbon intensity forecasting and carbon aware scheduling of ai workloads

The Large Language Models have very powerful features that completely changed the artificial intelligence scenario, but at the same time, their enormous computational demands are the source of serious environmental problems especially in terms of energy use and carbon release. The present day AI practices concentrate on speeding up the process without taking the environmental effect of the power sources into account. As a solution to this problem, this research proposes CarbonBalance framework. This framework introduces the integration of two components i.e. forecasting pipeline and a scheduling pipeline. The forecasting pipeline makes use of a machine learning pipeline where historical energy and weather data is processed. It adopts LightGBM for capturing complicated feature relations and Prophet for modeling temporal patterns in carbon intensity and provides accurate prediction of grid carbon levels for the scheduling pipeline, which uses an optimization scheduler and LLM profiles to make its decisions to direct non-urgent AI tasks to cleaner energy times and locations without affecting the critical tasks. Through this approach, the evaluated scheduling scenarios achieved a 41.6% reduction in carbon emissions, while critical workloads maintained zero scheduling delay. Thus, the framework supports more sustainable AI workload execution. Not applicable.