Instruction-based Prediction Techniques in Operating Systems

Опубликовано 6 сентября 2016, 5:36

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.