Where does all the time go?

Inside this Article Proudest accomplishments Early results Related Links Carnegie Mellon’s storage guru Video: Carnegie Mellon’s storage research center Sophisticated SAN change-management

The New Data Center model promises that one day your IT infrastructure will be self-managing. As executive director of Carnegie Mellon University's Parallel Data Laboratory and its state-of-the-art Data Center Observatory (DCO), Bill Courtright is working to make that promise a reality. Under his management, researchers at this Pittsburgh institution are exploring automated storage management. Their test bed, the DCO, also is a live data center serving university users. They call their work the Self-* (self star) Storage project, with the asterisk a placeholder for the words configuring, organizing, tuning, healing and, of course, managing. Courtright first must understand which management tasks consume the most time.

What are your goals for the Self-* Storage project?

Self-* Storage is the recognition that storage, especially as you get into distributed systems, is complex and difficult to understand. You don't build the storage and then after the fact jam a bunch of storage management on top of it and think it's going to work; you build the storage management in from Day One. So architecturally you make the system self-tuning, self-healing and so on - a part of the fabric of the design implementation.

How do the Self-* project and the DCO dovetail?

Self-* is being instrumented and is having control systems cooked in from the beginning that will really help us in the DCO research. And there's another piece to the DCO, which is managing the applications and how they get mapped onto the machines. So the [DCO IT and environmental systems], holistically when we look at them, will give us instrumentation and control over all hardware and software components - hardware meaning computers, valves, whatever, and software meaning file systems, operating systems, applications. Then we can look through that data and begin to infer trends, find opportunities for efficiencies that we wouldn't get in isolation looking at a file system or a server [in the IT infrastructure] or the air-conditioning system [in the physical infrastructure].