We describe a novel on-line file access predictor, Recent

We describe how to manage distributed file system caches based upon groups of files that are accessed together. We use file access patterns to automatically construct dynamic groupings of files and then manage our cache by fetching groups, rather than single files. We present experimental results, based on trace-driven workloads, demonstrating that grouping improves cache performance. At the file system client, grouping can reduce LRU demand fetches by 50 to 60%. At the server, cache hit rate improvements are much more pronounced, but vary widely (20 to over 1200%) depending upon the capacity of intervening caches. Our treatment includes information theoretic results that justify our approach to file grouping.

This study uses data gathered in a field test on 60 test participants to develop models that characterize driver brake perception–reaction times (PRTs), brake times, and stop–go decisions at the onset of a yellow indication at a high-speed signalized intersection approach. The study demonstrates that driver PRTs are influenced only by the driver's time to intersection (TTI) at the onset of the yellow indication. The driver PRT is found to increase linearly with TTI and is not affected by the vehicle speed (in the range of 54 to 88 km/h), driver gender, or driver age. In the case of stop–go behavior, the older driver (≥65 years of age) dilemma zone is wider, ranging from a TTI of 4.81 to 1.66 s versus 4.90 to 2.87 s for the younger age group. Female drivers are more likely to stop than male drivers and tend to have a dilemma zone that is closer to the intersection. Finally, the study demonstrates that dilemma zone control systems should consider a dilemma zone from 5.0 to 1.5 s instead of the current state of practice of 5.5 to 2.5 s to capture all potential driver age and gender groups.

Abstract-If the data density of magnetic disks is to continue its current 30-50% annual growth, new recording techniques are required. Among the actively considered options, shingled writing is currently the most attractive one because it is the easiest to implement at the device level. Shingled write recording trades the inconvenience of the inability to update in-place for a much higher data density by a using a different write technique that overlaps the currently written track with the previous track. Random reads are still possible on such devices, but writes must be done largely sequentially.In this paper, we discuss possible changes to disk-based data structures that the adoption of shingled writing will require. We first explore disk structures that are optimized for large sequential writes with little or no sequential writing, even of metadata structures, while providing acceptable read performance. We also examine the usefulness of non-volatile RAM and the benefits of object-based interfaces in the context of shingled disks. Finally, through the analysis of recent device traces, we demonstrate the surprising stability of written device blocks, with general purpose workloads showing that more than 93% of device blocks remain unchanged over a day, and that for more specialized workloads less than 0.5% of a shingled-write disk's capacity would be needed to hold randomly updated blocks.

Prediction is a powerful tool for performance and usability. It can reduce access latency for I/O systems, and can improve usability for mobile computing systems by automating the file hoarding process. We present recent research that has resulted in a file successor predictor that matches the performance of state-of-the-art contextmodeling predictors, while requiring a small fraction of their space requirements. Noah is an on-line algorithm for predicting successor file access events, effectively identifying strong pairings (successor relationships) among files. Noah can accurately predict approximately ¡ £¢ % of all file access events, while tracking only two candidate successors of which only one requires regular dynamic updates.

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