Analysis of nonpreemptive priority queues with multiple servers and two priority classes

Kao, Edward P. C.; Wilson, Sandra D.

doi:10.1016/s0377-2217(98)00280-x

Cited by 34 publications

(20 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general these yield complicated mathematical expressions susceptible to numerical instabilities at higher loads. See King and Mitrani [19]; Gail, Hantler, and Taylor [8,9]; Feng, Kowada, and Adachi [7]; and Kao and Wilson [11].…”

Section: Two Priority Classesmentioning

confidence: 99%

Multi-Server Queueing Systems with Multiple Priority Classes

et al. 2005

View full text Add to dashboard Cite

We present the first near-exact analysis of an M/PH/k queue with m > 2 preemptive-resume priority classes. Our analysis introduces a new technique, which we refer to as Recursive Dimensionality Reduction (RDR). The key idea in RDR is that the m-dimensionally infinite Markov chain, representing the m class state space, is recursively reduced to a 1-dimensionally infinite Markov chain, that is easily and quickly solved. RDR involves no truncation and results in only small inaccuracy when compared with simulation, for a wide range of loads and variability in the job size distribution.Our analytic results are then used to derive insights on how multi-server systems with prioritization compare with their single server counterparts with respect to response time. Multi-server systems are also compared with single server systems with respect to the effect of different prioritization schemes -"smart" prioritization (giving priority to the smaller jobs) versus "stupid" prioritization (giving priority to the larger jobs). We also study the effect of approximating m class performance by collapsing the m classes into just two classes.Keywords: M/GI/k, M/PH/k, multi-server queue, priority queue, matrix analytic methods, busy periods, multi-class queue, preemptive priority.

show abstract

Section: Two Priority Classesmentioning

confidence: 99%

Multi-Server Queueing Systems with Multiple Priority Classes

et al. 2005

View full text Add to dashboard Cite

show abstract

“…In general, these yield complicated mathematical expressions susceptible to numerical instabilities at higher loads, see King and Mitrani [19], Gail et al [8,9], Feng et al [7], and Kao and Wilson [13].…”

Section: Exponential Service Timesmentioning

confidence: 99%

How many servers are best in a dual-priority system?

Wierman¹,

Osogami²,

Scheller‐Wolf³

2006

Performance Evaluation

View full text Add to dashboard Cite

“…Based on the MCMS model, average delay for two kinds of traffics and loss rate for real-time traffics can be acquired. Such models have been studied thoroughly in Mitrani and King (1981), Gail et al (1988), Gail et al (1992), Kao and Narayanan (1990), Kao and Wilson (1999), Kella and Yechiali (1985), Wagner (1994Wagner ( , 1997, Davis (1966), Kapadia et al (1984) and Movaghar (1998), so we will not go into the details on getting the solutions of this model. 3.…”

Section: Performance Of Network Trafficmentioning

confidence: 99%

A heuristic adaptive QoS prediction scheme in single-hop passive star coupled WDM optical networks

Huang

2011

Journal of Network and Computer Applications

View full text Add to dashboard Cite

Analysis of nonpreemptive priority queues with multiple servers and two priority classes

Cited by 34 publications

References 15 publications

Multi-Server Queueing Systems with Multiple Priority Classes

Multi-Server Queueing Systems with Multiple Priority Classes

How many servers are best in a dual-priority system?

A heuristic adaptive QoS prediction scheme in single-hop passive star coupled WDM optical networks

Contact Info

Product

Resources

About