Research Highlight: A Machine Learning Cooldown for the Data Center
In recent years, the race to build the fastest computers has been joined by a parallel competition to design the most energy-efficient machines. The colossal data centers supporting cloud computing and web applications consume massive amounts of energy, using electricity to both run and cool their tens of thousands of servers. As engineers look for new CPU designs that reduce energy usage, scientists from Northwestern University and Argonne National Laboratory are seeking an AI-based solution, using the cloud computing testbed Chameleon to reduce power through smarter task traffic.The collaboration, COOLR, was created to discover new methods of reducing the energy and cooling costs of high-performance computing. New research from Northwestern PhD student Kaicheng Zhang tested whether smarter task placement — the assignment of computing jobs to specific servers in a cluster or data center –…
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