Analysis of a finite capacity non preemptive priority queue
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Cited by (29)
Performance analysis of space–time priority queues
2019, Performance EvaluationCitation Excerpt :Several authors have studied push-out buffers in the absence of time priority. Cheng and Akyildiz [17] and Lee et al. [18] consider a push-out buffer with Poisson arrival streams and general service time distributions, while Kapadia et al. [19] analyze the multi-server push-out buffer. In contrast to existing literature and motivated by the requirements of streaming and elastic traffic, the present paper investigates the push-out priority buffer, where space and time-priority are given to different classes.
A heuristic adaptive QoS prediction scheme in single-hop passive star coupled WDM optical networks
2011, Journal of Network and Computer ApplicationsA performance model for differentiated service over single-hop passive star coupled WDM optical networks
2011, Journal of Network and Computer ApplicationsCitation Excerpt :There are also some research results published on the performance model of the optical networks with single server priority queuing systems. Such models can be found in Mitrani and King (1981), Gail et al. (1988, 1992), Kao and Narayanan (1990), Kao and Wilson (1999), Kella and Yechiali (1985), Wagner (1994, 1997), Davis (1966), and Kapadia et al. (1984). However, the network performance modeled by multi-server priority queuing systems has received less attention.
How many servers are best in a dual-priority M / PH / k system?
2006, Performance EvaluationPriority queueing with finite buffer size and randomized push-out mechanism
2005, Performance EvaluationCitation Excerpt :In particular, we show that with the randomized push-out it is easy to control the loss probability of priority packets in a very large range. Furthermore, in the particular case of non-randomized push-out we obtain explicit analytic expressions for the loss probabilities that are simpler than the recurrent expressions in Kapadia et al. [12,13]. Finally we present some numerical examples and compare the randomized push-out scheme with the threshold based push-out scheme [8].
Waiting times of a finite-capacity multi-server model with non-preemptive priorities
1997, European Journal of Operational Research
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Asha S. Kapadia is an Associate Professor of Biometry and Health Services Administration at the University of Texas School of Public Health in Houston. Prior to joining the School of Public Health, she was an Associate Professor of Quantitative Management Sciences in the College of Business Administration at the University of Houston. She has also been a management consultant at Arthur D. Little Inc., Cambridge, Mass. Dr. Kapadia holds an S.M. degree in Industrial Management from the Massachusetts Institute of Technology and a Ph.D. in Statistics/Operations Research from Harvard University.
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A. Cameron Mitchell is an Associate Professor in Quantatitive Management Science in the College of Business, University of Houston. He received his B.A. degree from the University of the South (Sewanee) in economics and his M.B.A. and Ph.D. degrees from the University of Texas in statistics and operations research. His research interests lie mainly in the areas of simulation and time series analysis.