Studying Systems as Artifacts
Adam J. Oliner, Stanford University

Imperfections are an unavoidable characteristic of complex systems; the costs of these imperfections make it imperative for us to devise generic methods for effectively detecting and isolating them. Toward this end, we present a technique that infers the dependency structure of a system by looking for anomalous behavior correlated in time across components. I'll present some early results on a supercomputer and an autonomous vehicle, as well as provide a motivational survey of my work on system management: job scheduling, quality of service guarantees, checkpointing, and log analysis.

Adam Oliner is a third-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 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 supercomputers logs at Sandia National Labs.

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