Abstract: With a large percentage of total system cost going to system administration tasks, self-management remains a difficult and important goal in systems. As a step towards the self-management vision, I will present a framework we have developed that enables systems to be self-predicting and answer ``what-if'' questions about their behavior with little or no administrator involvement. We have built a Resource Advisor inside two real systems: Microsoft's SQL Server database and the Ursa Minor storage system at Carnegie Mellon University. The Resource Advisor helps with upgrade and data placement decisions and provides what-if interfaces to external administrators (and internal tuning modules). The Resource Advisor is based on efficient system behavioral models that enable robust predictions in multi-tier systems.

Short Bio: Eno Thereska is a PhD candidate at Carnegie Mellon University working with Prof. Greg Ganger. Eno has broad research interests in computer systems. Currently he is investigating ways to make the management of distributed systems easier. An approach he is currently pursuing puts sufficient instrumentation and modeling within the system, enabling it to answer several important what-if questions without outside intervention. He is interested in applying methods from queuing analysis (for components build from scratch) and machine learning (for legacy components) to this problem. As a testbed he is using Ursa Minor, a cluster-based storage system being deployed at Carnegie Mellon for researching system management issues. Concrete what-if questions in this system are about the effect of resource upgrades, service migration and data distribution. Eno received the Masters of Science (MS) degree in Electrical and Computer Engineering in 2003 at Carnegie Mellon University and the Bachelor of Science (BS) degree in Electrical and Computer Engineering and Computer Science in 2002 also at CMU.