Thursday, September 16, 2004
Noon - 1 pm
Hamerschlag Hall D-210
Challenges in Building a Two-Tiered Learning Architecture for Disk Layout
Choosing the correct settings for large systems can be a daunting task. The performance of the system is often heavily dependent upon these settings, and the ``correct'' settings are often closely coupled with the workload. System designers usually resort to using a set of heuristic approaches that are known to work well in some cases. However, hand-combining these heuristics is painstaking and fragile. We propose a two-tiered architecture that makes this combination transparent and robust, and describe an application of the architecture to the problem of disk layout optimization. This two-tiered architecture consists of a set of independent heuristics, and an adaptive method of combining them. However, building such a system has proved to be more difficult than expected. Each heuristic depends heavily on decisions from other heuristics, making it difficult to break the problem into smaller pieces. This talk outlines our approaches and how they have worked, and discusses the biggest challenges in building the system. Whether this problem is solvable is still open to debate, but the experiences reported provide a cautionary tale; system policy automation is complex and difficult.
Brandon Salmon just received his Master's Degree in Computer Engineering at Carnegie Mellon University. He recieved his Bachelors in Computer Science at Stanford. He is currently working on the self-* project with his advisor Greg Ganger. His research focuses on building systems that can adapt to their surroundings, allowing them to operate effectively with less human intervention.
Contact Linda Whipkey, Karen
Lindenfelser or visit http://www.pdl.cmu.edu/SDI/