Stochastic Petri nets (SPNs) and extensions are a popular method for evaluating a wide variety of systems. In most cases, the interesting measures regarding the system's characteristics can be defined at the net level by means of reward variables. Depending on the measures, these net-level reward models are solved either by first generating a state-level reward model or by directly generating paths from the net-level description. In this thesis, we propose algorithms for the generation of state-level reward models as well as for directly obtaining solutions from net-level reward models when the net-level reward models are specified as stochastic activity networks (SANs) with "step-based reward structure." Moreover, we propose algorithms for computing the expected value and the probability distribution function of a reward variable at specified time instants, and for computing the probability distribution function of reward accumulated during a finite interval. The interval may correspond to the mission period in a mission-critical system, the time between scheduled maintenances, or a warranty period; whereas the time instants may be critical instances during these intervals. The proposed algorithms avoid the construction of state-level representations and the memory growth problems experienced when applying previous approaches to large models. Furthermore, we study the effect of workload on the availability and response time of voting algorithms. Voting algorithms are a popular way to provide data consistency in replicated data systems. Many models have been made to study the degree to which replication increases the availability of data, and some have been made to study the cost incurred in maintaining consistency. However, little work has been done to evaluate the time it takes to serve request, accounting for server and network failures, or to determine the effect of workload on these measures. In this thesis, we use stochastic activity networks (SANs) to study the effect of work load on availability and mean response time of two variant models of a replicated file system to maintain data consistency, one using a static voting algorithm, the other using a dynamic voting algorithm.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/290583 |
| Date | January 1996 |
| Creators | Qureshi, Muhammad Akber, 1964- |
| Contributors | Sanders, William H. |
| Publisher | The University of Arizona. |
| Source Sets | University of Arizona |
| Language | en_US |
| Detected Language | English |
| Type | text, Dissertation-Reproduction (electronic) |
| 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. |
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