A stochastic process model for transient trace data

Simple item page
dc.contributor.authorMathur, Anupen
dc.contributor.committeechairAbrams, Marcen
dc.contributor.committeememberArthur, James D.en
dc.contributor.committeememberBalci, Osmanen
dc.contributor.committeememberFoutz, Robert V.en
dc.contributor.committeememberShaffer, Clifford A.en
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2014-03-14T21:20:27Zen
dc.date.adate2007-10-05en
dc.date.available2014-03-14T21:20:27Zen
dc.date.issued1996en
dc.date.rdate2007-10-05en
dc.date.sdate2007-10-05en
dc.description.abstractCreation of sufficiently accurate workload models of computer systems is a key step in evaluating and tuning these systems. Workload models for an observable system can be built from traces collected by observing the system. This dissertation presents a novel technique to construct non-executable, artificial workload models fitting transient trace data. The trace can be a categorical or numerical time-series. The trace is considered a sample realization of a non-stationary stochastic process, {X<sub>t</sub>}, such that random variables X<sub>t</sub> follow different probability distributions. To estimate the parameters for the model a Rate Evolution Graph (REG) is built from the trace data. The REG is a two-dimensional Cartesian graph which plots the number of occurrences of each unique state in the trace on the ordinate and time on the abscissa. The REG contains one path for all instances of each unique state in the trace. The derivative of a REG path at time t is used as an estimate of the probability of occurrence of the corresponding state at t. We use piecewise linear regression to fit straight line segments to each REG path. The slopes of the line segments that fit a REG path estimate the time dependent probability of occurrence of the corresponding state. The estimates of occurrence probabilities of all unique states in the trace are used to construct a time-dependent joint probability mass function. The joint probability mass function is the representation of the Pzrecewise Independent Stochastic Process model for the trace. Two methods that assist to compact the model, while retaining accuracy, are also discussed.en
dc.description.degreePh. D.en
dc.format.extentxvii, 174 leavesen
dc.format.mediumBTDen
dc.format.mimetypeapplication/pdfen
dc.identifier.otheretd-10052007-143230en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-10052007-143230/en
dc.identifier.urihttp://hdl.handle.net/10919/39643en
dc.language.isoenen
dc.publisherVirginia Techen
dc.relation.haspartLD5655.V856_1996.M3798.pdfen
dc.relation.isformatofOCLC# 36092988en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectworkload modelsen
dc.subjectprogram behavioren
dc.subjectcategorical dataen
dc.subject.lccLD5655.V856 1996.M3798en
dc.titleA stochastic process model for transient trace dataen
dc.typeDissertationen
dc.type.dcmitypeTexten
thesis.degree.disciplineComputer Scienceen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
LD5655.V856_1996.M3798.pdf
Size:
7.2 MB
Format:
Adobe Portable Document Format
Description:
Download

Collections

Doctoral Dissertations