Optimizing Compute Allocation and Cooling Usage to Reduce Electricity Bills and Carbon Emissions
Author(s): Hema Vamsi Nikhil Katakam
Publication #: 2601006
Date of Publication: 17.01.2026
Country: United States
Pages: 1-6
Published In: Volume 12 Issue 1 January-2026
DOI: https://doi.org/10.62970/IJIRCT.v12.i1.2601006
Abstract
Data centers form the computational foundation of the modern digital economy but also contribute significantly to global electricity consumption and carbon emissions. A substantial portion of this energy, nearly 40%, is consumed by cooling systems that maintain operational stability. This paper conceptually presents an AI-driven framework for energy-efficient cloud data centers that jointly optimizes compute allocation and cooling usage. By integrating predictive analytics, reinforcement learning, and carbon-aware scheduling, the proposed approach enables dynamic workload distribution and intelligent thermal management in real time. The model leverages system telemetry, environmental data, and carbon-intensity forecasts to make holistic decisions that minimize both operational costs and environmental impact. The framework offers a pathway toward sustainable, self-optimizing data centers capable of reducing total power consumption and CO₂ emissions by up to 20%, contributing to global net-zero goals and the sustainable evolution of digital infrastructure.
Keywords: data centers; compute allocation; cooling optimization; reinforcement learning; carbon-aware computing.
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