Model-based approaches are attractive here, because they can incorporate human knowledge about the system, and do not require labeled failure data. A classical family of models of computer performance is queueing models. Queueing models predict the explosion in system latency under high workload in a way that is often reasonable for real systems.
In this talk, we present a novel graphical modeling viewpoint on queueing models, which allows them to be used for inference about past system behavior and learning from incomplete data. The idea is to measure a small set of arrival and departure times from the system, treating the times that were not measured as missing data. The posterior distribution over missing data and parameters can then be sampled using Markov chain Monte Carlo techniques. Developing a sampler in this case is significantly more challenging than for standard graphical models, because of the complex deterministic dependencies that arise in queueing models. On data from a benchmark Web 2.0 application, we demonstrate the ability to localize performance problems with 25% of the measurement overhead.
Bio: Charles Sutton is a postdoctoral researcher at the University of California Berkeley, working under the supervision of Michael I. Jordan. He received his Ph.D. in 2008 from the University of Massachusetts Amherst, working with Andrew McCallum. His research interests include graphical models, structured prediction, natural language processing, and the application of machine learning methods to computer systems.
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