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    Deep reinforcement learning optimizes data center cooling, reducing energy costs by up to 15%. This intelligent control system enhances operational efficiency and thermal safety in data centers.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Sustainable Computing

    Background:

    • Data centers (DCs) are critical for modern services like e-commerce and cloud computing.
    • Cooling constitutes nearly half of a DC's energy cost, presenting a significant operational challenge.
    • Existing cooling control methods often rely on heuristics and expert knowledge, leading to generalization issues and suboptimal performance.

    Purpose of the Study:

    • To propose an optimized data center cooling control algorithm (CCA) using deep reinforcement learning (DRL).
    • To address the challenge of reducing cooling energy consumption without compromising thermal safety.
    • To develop an end-to-end DRL-based solution for automated and intelligent DC operations.

    Main Methods:

    • An off-policy offline deep deterministic policy gradient (DDPG) algorithm is employed for end-to-end cooling control.
    • An evaluation network predicts DC energy cost and cooling effects, while a policy network determines optimal control settings.
    • A de-underestimation (DUE) validation mechanism is introduced to mitigate risk underestimation in the critic network.

    Main Results:

    • The proposed CCA achieved up to 11% cooling cost reduction on the EnergyPlus simulation platform.
    • In a real-world trace-based study at the National Super Computing Centre (NSCC) Singapore, the algorithm demonstrated approximately 15% cooling energy savings.
    • The DRL approach proved effective in optimizing DC cooling control compared to baseline methods.

    Conclusions:

    • Deep reinforcement learning offers a powerful framework for optimizing data center cooling control.
    • The proposed CCA provides an intelligent, automated solution for reducing energy consumption and operational costs in data centers.
    • This pioneering approach has the potential to revolutionize digital infrastructure management through AI-driven automation.