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.
@InProceedings{chis_et_al:OASIcs.ICCSW.2012.29, author = {Chis, Tiberiu S. and Harrison, Peter G.}, title = {{Incremental HMM with an improved Baum-Welch Algorithm}}, booktitle = {2012 Imperial College Computing Student Workshop}, pages = {29--34}, series = {Open Access Series in Informatics (OASIcs)}, ISBN = {978-3-939897-48-4}, ISSN = {2190-6807}, year = {2012}, volume = {28}, editor = {Jones, Andrew V.}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2012.29}, URN = {urn:nbn:de:0030-drops-37613}, doi = {10.4230/OASIcs.ICCSW.2012.29}, annote = {Keywords: hidden Markov model, Baum-Welch algorithm, Backward algorithm, discrete Markov arrival process, incremental workload model} }
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