Using probabilistic reasoning to automate software tuning

Authors:
David G. Sullivan;Margo I. Seltzer;Avi Pfeffer
Affiliations:
Harvard University, Cambridge, MA;Harvard University, Cambridge, MA;Harvard University, Cambridge, MA
Venue:
Proceedings of the joint international conference on Measurement and modeling of computer systems
Year:
2004

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Abstract

Manually tuning the parameters or "knobs" of a complex software system is an extremely difficult task. Ideally, the process of software tuning should be automated, allowing software systems to reconfigure themselves as needed in response to changing conditions. We present a methodology that uses a probabilistic, graphical model known as an influence diagram as the foundation of an effective, automated approach to software tuning. We have used our methodology to simultaneously tune four knobs from the Berkeley DB embedded database system, and our results show that an influence diagram can effectively generalize from training data for this domain.