Input/output access pattern classification using hidden Markov models
Tara M. Madhyastha, Daniel A. Reed · 1997
Input/output performance on current parallel file systems is sensitive to a good match of application access pattern to file system capabilities.Automaticiuput/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 &n-rework, presenting performance results from parallel and sequential benchmarks and applications.