MIT's Delimitrou applies ML to curb data centers' energy drain
Christina Delimitrou, a newly tenured associate professor in MIT's Department of Electrical Engineering and Computer Science and a member of CSAIL, and her group apply machine learning to resource management in large-scale data centers, redesigning cloud systems and server architectures so operators can squeeze more computing power out of existing hardware. She says many large computing systems ran at only about 15 percent utilization, with considerable bloat especially on the software side, and that removing that bloat without hurting performance would reduce the need to build new data centers. The group has also built Seer, which uses deep learning to anticipate and prevent web application failures, and Ditto, which mimics a proprietary application's structure and performance so academic teams can study commercial systems they cannot access.
Data center power demand and grid strain are becoming a real constraint on AI expansion, so research aimed at raising utilization could shape how much new power and compute infrastructure gets built.