Author
Obal, Walter Douglas, 1966-Issue Date
1998Advisor
Marcellin, Michael W.Sanders, William H.
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The University of Arizona.Rights
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.Abstract
Much work has been done on the problem of stochastic modeling for the evaluation of performance, dependability and performability properties of systems, but little attention has been given to the interplay between the model and the performance measure of interest. Our work addresses the problem of automatically constructing Markov processes tailored to the structure of the system and the nature of the performance measures of interest. To solve this problem, we have developed new techniques for detecting and exploiting symmetry in the model structure, new reward variable specification techniques, and new state-space construction procedures. We propose a new method for detecting and exploiting model symmetry in which (1) models retain the structure of the system, and (2) all symmetry inherent in the structure of the model can be detected and exploited for the purposes of state-space reduction. Then, we extend the array of performance measures that may be derived from a given system model by introducing a class of path-based reward variables, which allow rewards to be accumulated based on sequences of states and transitions. Finally, we describe a new reward variable specification formalism and state-space construction procedure for automatically computing the appropriate level of state-space reduction based on the nature of the reward variables and the structural symmetry in the system model.Type
textDissertation-Reproduction (electronic)
Degree Name
Ph.D.Degree Level
doctoralDegree Program
Graduate CollegeElectrical and Computer Engineering