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Poster
Fast Variational Inference for Large-scale Internet Diagnosis
John C Platt · Emre Kiciman · David A Maltz

Mon Dec 03 10:30 AM -- 10:40 AM (PST) @ None #None

Web servers on the Internet need to maintain high reliability, but the cause of intermittent failures of web transactions is non-obvious. We use Bayesian inference to diagnose problems with web services. This diagnosis problem is far larger than any previously attempted: it requires inference of 10^4 possible faults from 10^5 observations. Further, such inference must be performed in less than a second. Inference can be done at this speed by combining a variational approximation, a mean-field approximation, and the use of stochastic gradient descent to optimize a variational cost function. We use this fast inference to diagnose a time series of anomalous HTTP requests taken from a real web service. The inference is fast enough to analyze network logs with billions of entries in a matter of hours.

Author Information

John C Platt (Microsoft Research)
Emre Kiciman (Microsoft Research)
David A Maltz (Microsoft)

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