Physics-informed machine learning for green data center operations - Wang, Ruihang (Nanyang Technological University, 2023)
The data center (DC) industry is rapidly growing in recent years to meet the ever-increasing cloud computing and storage demands. The dramatically increasing DC scale brings substantial challenges for DC operations that aim to maintain business continuity and reduce operating costs. Current DCs are mostly operated in a reactive manner in that they adopt feedback controllers and rely on empirical best practices. However, existing operating principles only focus on maintaining temperatures within certain ranges without taking system power usage into account. To achieve low-power DC operations, proactive and intelligent solutions are highly desirable. Machine learning (ML) approaches based on deep neural networks have been considered for developing such solutions. However, applying these advanced ML algorithms to DC operations faces two major challenges. First, ML often requires a large volume of training data, including those from abnormal cases which are hard to obtain from a stably operated DC. Second, the prevailing risk-aversion mindset in the DC industry hinders the wide deployment of ML-based solutions. To unleash the potential of ML for DC operations, this thesis proposes to integrate DC's ``physics priors" into the learning and deployment of the ML algorithms. The proposed physics-informed ML solutions advance DC operations in the following three stages.