Directing the Datacenter with Machine Learning

At the RAD Lab we are prototyping forward-looking datacenter software architectures using a three-pillar approach. The first pillar is exploiting application frameworks and languages optimized for high programmer productivity such as Ruby on Rails. Second is the deployment of machine learning to identify performance and scalability bottlenecks, create dynamic models for predicting performance, and mining runtime telemetry as well as console logs to identify operational problems; a framework we call the Director provides a closed “observe/analyze/act” loop into which these algorithms can be inserted. Third is a new persistent storage abstraction, SCADS (Scalable Consistency-Adjustable Data Store) designed specifically for the needs of datacenter-scale interactive applications, exposing consistency tradeoffs explicitly to the application developer in the context of an object-graph storage model deliberately similar to that provided by Rails’ ActiveRecord.

Speaker Details

Armando Fox recently joined UC Berkeley as a co-founder of the Berkeley RAD Lab. Prior to that he was an Assistant Professor of Computer Science at Stanford. His recent collaboration with David Patterson on Recovery-Oriented Computing earned him the distinction of being included in the “Scientific American 50” of 2003; he is also the recipient of an NSF CAREER award and teaching award from Stanford University, the Society of Women Engineers, and Tau Beta Pi. In previous lives he helped design the Intel Pentium Pro microprocessor and founded a small company to commercialize his UC Berkeley dissertation research on mobile computing. He received his other degrees in electrical engineering and computer science from MIT and the University of Illinois.

Date:
Speakers:
Armando Fox
Affiliation:
University of California, Berkeley