A green partnership of academia and industry look at data center efficiencies

Three universities team with the likes of IBM, Microsoft and Facebook to tackle energy efficiencies, starting with the data center.


I love to see the tech industry partner with higher ed institutions. There are so many good reasons for these types of collaborations: industry can positively impact the up-and-coming workforce, universities can parlay their research into reality, research gets much needed funding, innovation occurs.

So I thought I’d share the news about this collaboration, a partnership between three universities and 15 companies, all working together on a research center focused on energy efficiencies for the electronics industry.

The initiative – dubbed the Industry/University Cooperative Research Center in Energy-Efficient Electronic Systems, or the NSF I/UCRC E3S Center – is funded in part by the National Science Foundation (NSF), but the primary support will come from 15 companies including Microsoft, IBM, Facebook, General Electric, Corning Inc., Emerson Network Power and Emerson Delaware Valley Liebert, Verizon and Comcast.

Binghamton University, State University of New York and its partners Villanova University and the University of Texas at Arlington are the three universities involved. The research initiative is focusing on developing methods for operating electronic systems and cooling equipment so they can be dynamic self-sensing and self-regulating systems that are predictive, stable and verified in real time. Computer scientists and mechanical and electrical engineers will all work together on the research.

In this article in Discover-e, Binghamton University’s online site, Bahgat Sammakia, E3S director and interim VP for research at Binghamton, said the value of the initiative is to allow researchers to look at energy efficiency problems holistically, “from all angles and across many disciplines,” to find solutions.

Out of the gate, E3S will zero in on data centers. Researchers will work on developing algorithms to control cooling resources and to assist expert system schedulers to schedule and/or migrate workload and simultaneously adjust the cooling system output to achieve optimal energy consumption. They will also look at policies around scheduling and workload prediction and management that can be interpreted and enforced at the software level, including kernel scheduling policies to operate the IT equipment within pre-specified energy limits.

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