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Deep Reinforcement Learning Towards Real-world Dynamic Thermal Management of Data Centers by Qingang Zhang; Wei Zeng; Qinjie Lin; Chin-Boon Chng; Chee-Kong Chui; Poh-Seng Lee is a Engineering article available to read on EtoBox.

What is Deep Reinforcement Learning Towards Real-world Dynamic Thermal Management of Data Centers about?

Deep Reinforcement Learning has been increasingly researched for Dynamic Thermal Management in Data Centers. However, existing works typically evaluate the performance of algorithms on a specific task, utilizing models or data trajectories without discussing in detail their implementation feasibility and their ability to deal with diverse work scenarios. The lack of these works limits the real-world deployment of Deep Reinforcement Learning. To this end, this paper comprehensively evaluates the strengths and limitations of state-of-the-art algorithms by conducting analytical and numerical studies. The analysis is conducted in four dimensions: algorithms, tasks, system dynamics, and knowledge transfer. As an inherent property, the sensitivity to algorithms settings is first evaluated in a simulated data center model. The ability to deal with various tasks and the sensitivity to reward functions are subsequently studied. The trade-off between constraints and power savings is identified by conducting ablation experiments. Next, the performance under different work scenarios is investigated, including various equipment, workload schedules, locations, and power densities. Finally, the t

Who reads Deep Reinforcement Learning Towards Real-world Dynamic Thermal Management of Data Centers?

Author

Qingang Zhang; Wei Zeng; Qinjie Lin; Chin-Boon Chng; Chee-Kong Chui; Poh-Seng Lee

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)