Input/output access pattern classification using hidden Markov models

Abstract

Input/output performance on current parallel file systems is sensitive to a good match of application access pattern to file system capabilities. Automatic input/output access classification can determine application access patterns at execution time, guiding adaptive file system policies. In this paper we examine a new method for access pattern classification that uses hidden Markov models, trained on access patterns from previous executions, to create a probabilistic model of input/output accesses. We compare this approach to a neural network classification framework, presenting performance results from parallel and sequential benchmarks and applications.