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Computer Science @ Rochester
Monday, December 05, 2005
11:00 AM
CSB 209
Y. Charlie Hu
Purdue U.
Program-Counter-Based Prediction Techniques in Operating System
Program instructions uniquely identified by their program counters (PCs) provide a convenient and accurate means of recording the context of program execution and PC-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 PC-based prediction for resource management. This work explores the potential benefits provided by PC-based prediction in operating systems(PCOS). In particular, we investigate the potential of using PC-based prediction techniques for managing I/O devices in operating systems.

As a first demonstration of PCOS, we developed a PC-based access pattern classification technique (PCC) for buffer cache management. PCC allows the operating system to correlate the I/O operations with the program context in which they are issued via the PCs 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 have also developed a PC-based technique (PCAP) 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. PCAP 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.