Google Funds 'Radical' Efficiency Research
Google has awarded a $1 million grant to researchers developing low-energy modes on servers, part of a larger funding of university research projects aimed at slashing energy use of Internet data centers, including "radical experiments" in data center testbeds.
February 10, 2010
Google has awarded a $1 million grant to researchers developing low-energy modes on servers, part of a larger funding of university research projects aimed at slashing energy use of Internet data centers, including "radical experiments" in data center test facilities.
The $1 million award for the Greenscale Center for Energy-Efficient Computing at the University of California at Santa Barbara was the largest of 12 grants from the Google Focused Research Awards, which included support for machine learning, data collection via mobile devices and privacy, as well as computer energy efficiency.
The Greenscale award went to researchers Ricardo Bianchini of Rutgers, Fred Chong of UC Santa Barbara, Thomas Wenisch of the University of Michigan and Sudhanva Gurumurthi of the University of Virginia. The team will create a testbed to validate their concepts.
'Radical Experiments'
“Greenscale will provide critical infrastructure to the project with the planned construction of the Greenscale Experimental Datacenter, a state-of-the-art miniature data center where systems researchers can conduct radical experiments not possible in production data centers,” said Fred Chong, professor of computer science at UCSB and the center’s director.
Bianchini has been studying computer energy efficiency since 2000, and is focusing on inefficiencies in the way servers use power. “Data centers have to be built to handle the highest anticipated demand,” he said. “But most of the time, they are only running between 20 and 50 percent of capacity. Trouble is, the computer servers in these centers consume about the same amount of energy whether their workload is low or high.”
The team will explore ways to create low power modes in servers, allowing parts of the computer to be turned off while other parts remain accessible. The goal is to allow less active servers to move their processing loads to other servers and essentially go to sleep. But information on the sleeping servers’ memories must still be instantly accessible.
Sleep Mode and Memory Access
In current computer designs, data requests go through a memory controller that is part of the central processing unit, or CPU. If that CPU is asleep, it cannot provide that access. The GreenScale team is seeking to redesign CPUs with a separate power feed to the memory controller, allowing it to perform the needed memory management functions while the rest of the CPU stays asleep. The goal of such redesigns would be to conserve 40 to 50 percent of the power that servers now consume.
The $1 million research grant from Google will fund two years of research, at which point Google will review the team's progress and may award an additional $500,000 for a third year.
Other teams that received funding for research on energy efficiency in computing include:
A Carnegie Mellon University team of computer science professors David Andersen and Mor Harchol-Balter will receive $100,000 to study scaling problems associated with building energy-efficient computing clusters.
A University of Michigan team composed of Thomas Wenisch, Trevor Mudge, David Blaauw and Dennis Sylvester. Wenisch and Mudge are conducting research into the use of disaggregated memory to enhance the energy efficiency of data centers. Disaggregation refers to the separation of memory from other data center server components and linking together of those components through an interconnect.
A Stanford University team including VMware co-founder Mendel Rosenblum, Christos Kozyrakis, Mark Horowitz, Benjamin Lee, and Nick McKeown.
A project from Tajana Simunic Rosing, Steven Swanson and Amin Vahdat from the University of California at San Diego.
Princeton University researchers Margaret Martonosi, Jennifer Rexford, Michael Freedman and Mung Chiang
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