2 Search Results for "Harrison, Peter G."

Document
DOI: 10.4230/OASIcs.ICCSW.2012.29
Incremental HMM with an improved Baum-Welch Algorithm

Authors: Tiberiu S. Chis and Peter G. Harrison

Published in: OASIcs, Volume 28, 2012 Imperial College Computing Student Workshop

Abstract
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.
Cite as

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)

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@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-dev.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}
}
Document
DOI: 10.4230/OASIcs.ICCSW.2012.75
Collecting battery data with Open Battery

Authors: Gareth L. Jones and Peter G. Harrison

Published in: OASIcs, Volume 28, 2012 Imperial College Computing Student Workshop

Abstract
In this paper we present Open Battery, a tool for collecting data on mobile phone battery usage, describe the data we have collected so far and make some observations. We then introduce the fluid queue model which we hope may prove a useful tool in future work to describe mobile phone battery traces.
Cite as

Gareth L. Jones and Peter G. Harrison. Collecting battery data with Open Battery. In 2012 Imperial College Computing Student Workshop. Open Access Series in Informatics (OASIcs), Volume 28, pp. 75-80, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)

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@InProceedings{jones_et_al:OASIcs.ICCSW.2012.75,
  author =	{Jones, Gareth L. and Harrison, Peter G.},
  title =	{{Collecting battery data with Open Battery}},
  booktitle =	{2012 Imperial College Computing Student Workshop},
  pages =	{75--80},
  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-dev.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2012.75},
  URN =		{urn:nbn:de:0030-drops-37683},
  doi =		{10.4230/OASIcs.ICCSW.2012.75},
  annote =	{Keywords: battery model, battery data}
}
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