Carbon-Aware Scheduling in Cloud Computing Operations: A Multi-Objective Optimisation Approach

Abstract

The rapid expansion of cloud computing has intensified the environmental impact of large-scale data centres, which now represent a significant portion of global electricity consumption. Traditional scheduling strategies typically optimise performance or cost, disregarding the fluctuating carbon intensity of regional power grids. This study proposes a dynamic carbon-aware scheduling framework that integrates real-time carbon intensity forecasting with multi-objective optimisation and adaptive rolling-horizon control. The proposed model simultaneously minimises operational cost and greenhouse gas emissions by intelligently shifting computational workloads across time and geography in response to renewable energy availability. The framework combines an ensemble forecasting module, using long short-term memory (LSTM) and gradient boosting regression, with a mixed-integer linear programming (MILP) model solved via the (Formula presented.) -constraint method. It adaptively updates scheduling decisions based on updated carbon forecasts and workload arrivals. Experimental validation on real datasets from the UK National Grid and Google Cloud workload traces demonstrates an average (Formula presented.) reduction in (Formula presented.) emissions, a (Formula presented.) improvement in cost efficiency and less than (Formula presented.) performance degradation compared to conventional schedulers. Pareto front analysis further reveals actionable trade-offs between economic efficiency and environmental sustainability. The results confirm that integrating operational research with carbon intelligence enables cloud infrastructures to become both cost-effective and climate-aligned.