Carnegie Mellon University researchers have created the Data Center Observatory (DCO), a research vehicle for the study of data center automation and efficiency. The project is a collaborative effort between Carnegie Mellon and industry and government partners, including APC, whose equipment is being used in the center.
Energy efficiency is one of the center’s major thrusts, as high-density computing represents the industry’s primary operational challenge. “These large clusters of power-hungry machines, along with rising energy prices, are generating huge energy bills, forcing data center owners nationwide to seek more energy-efficient solutions,” said Greg Ganger, a professor of electrical and computer engineering and director of the Parallel Data Lab (PDL), a Carnegie Mellon organization specializing in the study of storage systems. To tackle these issues, university researchers are working with APC to develop new ways to reduce energy demands in data centers.
The 2,000-square-foot DCO has the ability to support 40 racks of computers, which would consume energy at a rate of up to 774 kW – more than the rate of consumption of 750 average-sized homes. In addition to studying dense computing environments, the DCO will support a variety of Carnegie Mellon research activities, from data mining to CAD/architecture, visualization and real networked services. The DCO joins Carnegie Mellon’s long tradition of weaving infrastructure research into campus life, which keeps the university at the forefront of technology.
Administration costs are another major research thrust. Data centers are complex to operate and require significant human administration support. “Anecdotally, we know that human costs are a dominant part of the total cost of ownership for data centers, but exactly where people spend their time isn’t well understood,” said Bill Courtright, executive director of the PDL. “One of the things that makes the DCO so interesting is that, for the first time, university researchers will be able to study human costs and efficiencies in a working data center.”