Reinforcement Learning–Based Optimization of Energy Consumption in Autonomous Data Centers
Keywords:
reinforcement learning, autonomous data centers, energy optimization, sustainability, governance, socio-technical systemsAbstract
The rapid expansion of cloud computing and large-scale data processing has led to an unprecedented increase in energy consumption within data centers, which now account for a significant fraction of global electricity use. Autonomous data centers, characterized by self-managing operational workflows, present both opportunities and challenges for energy optimization. This paper examines the application of reinforcement learning (RL) as a core paradigm for dynamically controlling energy consumption in such environments. Rather than focusing on algorithmic minutiae, we adopt a system-level perspective that integrates architectural design, computational governance, sustainability metrics, and policy implications. We discuss how RL-based agents can interact with heterogeneous infrastructure components—including cooling systems, server provisioning, workload scheduling, and power distribution—to achieve energy efficiency while maintaining performance guarantees. The analysis highlights structural trade-offs between energy savings, latency constraints, hardware wear, and reliability. We also explore the sociotechnical dimensions of deploying RL in critical infrastructure, including accountability, fairness in resource allocation, and alignment with environmental regulations. Through illustrative case studies and cross-domain comparisons, we demonstrate that RL-based optimization, when embedded within a robust governance framework, can yield substantial reductions in power usage effectiveness (PUE) and carbon footprint. The paper concludes with forward-looking perspectives on the integration of reinforcement learning with emerging technologies such as digital twins, federated learning, and edge computing, and offers recommendations for researchers, operators, and policymakers.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
