2009 Poster Sessions : Using Influence to Understand Complex Systems

Student Name : Adam Oliner
Advisor : Alex Aiken
Research Areas: Computer Systems
We propose a method for identifying the sources of problems in complex production systems where, due to the prohibitive costs of instrumentation, the data available for analysis may be noisy or incomplete. In particular, we may not have complete knowledge of all components and their interactions. We define 'influences' as a class of component interactions that includes direct communication and resource contention. Our method infers the influences among components in a system by looking for time-correlated divergence from models of individual component behavior. We summarize the strength and directionality of shared influences using a Structure-of-Influence Graph (SIG), which helps users quickly identify likely contributors to a problem.

Adam Oliner is a fourth-year PhD student in the Computer Science Department at Stanford University, working with Alex Aiken. He is a DOE High Performance Computer Science Fellow and former honorary Stanford Graduate Fellow. Before coming to Stanford, he earned a Master's of Engineering in electrical engineering and computer science at MIT, where he also received undergraduate degrees in computer science and mathematics. He interned several times at IBM with the Blue Gene/L system software team and spent a summer studying supercomputer logs at Sandia National Labs.