AI system optimally allocates workloads across thousands of servers to cut costs, save energy

A novel system developed by MIT researchers automatically "learns" how to schedule data-processing operations across thousands of servers--a task traditionally reserved for imprecise, human-designed algorithms. Doing so could help today's power-hungry data centers run far more efficiently. Data centers can contain tens of thousands of servers, which constantly run data-processing tasks from developers and users. Cluster scheduling algorithms allocate the incoming tasks across the servers, in real-time, to efficiently utilize all available computing resources and get jobs done fast. Traditionally, however, humans fine-tune those scheduling algorithms, based on some basic guidelines ("policies") and various tradeoffs.