Green Energy Management for Sustainable Data Centers Using Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning framework for optimizing energy management in data centers, effectively reducing costs and emissions while maintaining service quality.

Contribution

It develops a novel DRL-based approach with a hybrid LSTM and attention architecture, outperforming existing methods in cost reduction and efficiency.

Findings

Achieves 38% reduction in energy costs compared to heuristics.

Outperforms baseline DRL methods by 4.6%.

Maintains SLA violation rate as low as 1.5%.

Abstract

The exponential growth of digital services has positioned data centers among the most energy-intensive infrastructures in the modern economy, raising critical concerns regarding operational costs, carbon emissions, and the sustainable integration of renewable energy sources. This paper proposes a novel Deep Reinforcement Learning (DRL)-based energy management framework for data centers, designed to dynamically coordinate solar photovoltaic generation, wind power, battery storage systems, and conventional grid electricity under highly stochastic operational conditions. The proposed framework formulates the energy management problem as a Markov Decision Process and employs a Proximal Policy Optimization (PPO) agent augmented with a hybrid Long Short-Term Memory and temporal attention architecture, enabling accurate modeling of workload dynamics and renewable generation variability. A multi-objective reward function jointly minimizes energy costs, carbon emissions, and service-level agreement (SLA) violations while promoting efficient storage utilization. Extensive experiments conducted on three datasets demonstrate that the proposed framework achieves a 38\% reduction in energy costs compared to rule-based heuristics and outperforms the strongest DRL baseline by 4.6\%, while maintaining an SLA violation rate as low as 1.5\% and an energy efficiency of 83.7\%. Ablation studies confirm the individual contribution of each architectural component, and hyperparameter sensitivity analysis validates the robustness of the approach across a range of configurations.