Incremental HMM with an improved Baum-Welch Algorithm

Authors Tiberiu S. Chis, Peter G. Harrison

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Tiberiu S. Chis
Peter G. Harrison

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Tiberiu S. Chis and Peter G. Harrison. Incremental HMM with an improved Baum-Welch Algorithm. In 2012 Imperial College Computing Student Workshop. Open Access Series in Informatics (OASIcs), Volume 28, pp. 29-34, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)


There is an increasing demand for systems which handle higher density, additional loads as seen in storage workload modelling, where workloads can be characterized on-line. This paper aims to find a workload model which processes incoming data and then updates its parameters "on-the-fly." Essentially, this will be an incremental hidden Markov model (IncHMM) with an improved Baum-Welch algorithm. Thus, the benefit will be obtaining a parsimonious model which updates its encoded information whenever more real time workload data becomes available. To achieve this model, two new approximations of the Baum-Welch algorithm are defined, followed by training our model using discrete time series. This time series is transformed from a large network trace made up of I/O commands, into a partitioned binned trace, and then filtered through a K-means clustering algorithm to obtain an observation trace. The IncHMM, together with the observation trace, produces the required parameters to form a discrete Markov arrival process (MAP). Finally, we generate our own data trace (using the IncHMM parameters and a random distribution) and statistically compare it to the raw I/O trace, thus validating our model.
  • hidden Markov model
  • Baum-Welch algorithm
  • Backward algorithm
  • discrete Markov arrival process
  • incremental workload model


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