[Ml-stat-talks] Wed afternoon: Gideon Mann on ML and network architecture

David Mimno mimno at CS.Princeton.EDU
Tue Apr 30 15:10:26 EDT 2013

A Machine Learning talk cleverly disguised as a Systems talk:


Wednesday, May 1, 2013, 3:30 PM - 4:30 PM
Computer Science Small Auditorium (Room 105)
Profiling Latency in Deployed Distributed Systems
Gideon Mann, Google

Understanding the sources of latency within a deployed distributed system
is complicated. Asynchronous control flow, variable workloads, pushes of
new backend servers, and unreliable hardware all can make significant
contribution to a job's performance. In this talk, I'll present the work of
the Weatherman effort to build a profiling tool for deployed distributed
systems. The method uses distributed traces to estimate the code control
flow and predict/explain observed performance. I'll then illustrate how
this method has been applied to understand and tune large distributed
systems at Google and how it has been used in a differential profiling
fashion to understand the sources of latency changes.
To provide another view of latency, I'll quickly discuss our recent work on
distributed convex optimization with an emphasis on the interface between
the algorithm and the computing substrate performing the computation. In
particular, I'll show that data center architecture, in particular network
architecture, should have a significant impact on machine learning
algorithm design.

Gideon is a Staff Research Scientist at Google NY. He attended Brown
University as an undergraduate where he hung out in the AI lab and drank
too much Mountain Dew. He then attended graduate school at Johns Hopkins
University, worked in CLSP, and graduated in 2006 with a Ph.D. He still
misses Charm City. He then spent a post-doc at the UMass/Amherst with
Andrew McCallum working on weakly-supervised learning. In 2007, he joined

At Google, his team works on applied machine learning. The Weatherman
effort leverages statistical methods to data center management. The team
also is responsible for the Prediction API (
https://developers.google.com/prediction/). Publicly released in 2010,
Prediction was an early machine learning as a service offering and remains
an ongoing research project.
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