Issue No.04 - April (2004 vol.15)
Nancy Tran , IEEE
Daniel A. Reed , IEEE
<p><b>Abstract</b>—Inadequate I/O performance remains a major challenge in using high-end computing systems effectively. To address this problem, the paper presents TsModeler, an automatic time series modeling and prediction framework for adaptive I/O prefetching that uses ARIMA time series models to predict the temporal patterns of I/O requests. These online pattern analysis techniques and cutoff indicators for autocorrelation patterns enable multistep online predictions suitable for multiblock prefetching. This work also combines time series predictions with spatial Markov model predictions to determine when, what, and how many blocks to prefetch. Experimental results show reductions in execution time compared to the standard Linux file system across various hardware configurations.</p>
Input/output, adaptive prefetching, access patterns, performance modeling and prediction, time series analysis, pattern analysis, least squares methods, wavelets.
Nancy Tran, Daniel A. Reed, "Automatic ARIMA Time Series Modeling for Adaptive I/O Prefetching", IEEE Transactions on Parallel & Distributed Systems, vol.15, no. 4, pp. 362-377, April 2004, doi:10.1109/TPDS.2004.1271185