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@inproceedings{Chis:2012:10.4230/OASIcs.ICCSW.2012.29,
author = {Chis, TS and Harrison, PG},
doi = {10.4230/OASIcs.ICCSW.2012.29},
pages = {29--34},
title = {Incremental HMM with an improved baum-welch algorithm},
url = {http://dx.doi.org/10.4230/OASIcs.ICCSW.2012.29},
year = {2012}
}

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TY  - CPAPER
AB  - 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. © Tiberiu S. Chis and Peter G. Harrison.
AU  - Chis,TS
AU  - Harrison,PG
DO  - 10.4230/OASIcs.ICCSW.2012.29
EP  - 34
PY  - 2012///
SP  - 29
TI  - Incremental HMM with an improved baum-welch algorithm
UR  - http://dx.doi.org/10.4230/OASIcs.ICCSW.2012.29
ER  -

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