Instruction-based Prediction Techniques in Operating Systems

May 11, 2005
Chris Gniady | Purdue University

Program instructions uniquely identified by their program counters (PCs) provide a convenient and accurate means of recording the context of program execution and instruction-based prediction techniques have been widely used for performance optimizations at the architectural level. Operating systems, on the other hand, have not fully explored the benefits of instruction-based prediction for resource management. This research explores the potential benefits provided by instruction-based prediction in operating systems. In particular, we investigate the potential of using instruction-based prediction techniques for managing I/O devices in operating systems.

We first propose an instruction-based access pattern classification technique for buffer cache management. Our technique allows the operating system to correlate the I/O operations with the program context in which they are issued via the program counters of the call instructions that trigger the I/O requests. This correlation allows the operating system to classify I/O access pattern on a per-call-site basis, which achieves significantly better accuracy than previous per-file or per-application classification techniques.

We then propose an instruction-based technique for power management that dynamically learns the application I/O access patterns and associated disk idle times to predict when an I/O device can be shut down to save energy. The technique uses path-based correlation to observe a particular sequence of I/O triggering instructions leading to each idle period, and accurately predicts future occurrences of that idle period.

Speaker Details

Chris Gniady received his B.S. degree in Electrical and Computer Engineering from Purdue University in 1997. He is currently a Ph.D. candidate in the School of Electrical and Computer Engineering at Purdue, where he is a member of the Distributed Systems and Networking Lab. His research interests include operating systems, multiprocessor architecture, and architectural support for operating systems. His thesis research focuses on instruction-based prediction for I/O management in operating systems.