Date: Wednesday, 11 June 2008, 6:30 PM
Location: SAP LABS, Building D, 3410 Hillview Avenue,
Palo Alto, CA (Google
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Cost: Free and open to all who wish to attend, but membership is
only $10/year.
Topic
In a modern enterprise network of scale, dependencies between hosts and network services are surprisingly complex, typically undocumented, and rarely static. The automated discovery of these dependencies is a current challenge for network management and troubleshooting. In this talk I will present a method for addressing this challenge. The method is based on learning the parameters of a generative probabilistic model using little more than timings of packet transmission and reception, and then performing statistical hypothesis testing on the components of the model. I will also show results from applying this approach to real data from a trace collecting network events at Microsoft Research Cambridge. Joint work with Alex Simma, John MacCormick, Paul Barham, Richard Black, Rebecca Isaacs and Richard Mortier.
About the Speaker
Moises Goldszmidt is a principal researcher with Microsoft Research in the Silicon Valley Campus. Prior to Microsoft, Moises held similar positions with Hewlett-Packard Labs, SRI International, and Rockwell Science Center, and was a principal scientist with Peakstone Corporation (start-up). His research interests include probabilistic reasoning, graphical models, pattern recognition, statistical induction, machine learning, and artificial intelligence. Dr. Goldszmidt has a PhD degree in computer science from UCLA (1992).