A Clustering Approximation Technique for Queueing Network Models with a Large Number of Chains

Silva, De Souza E; Lavenberg,

doi:10.1109/tc.1986.1676784

Cited by 19 publications

(5 citation statements)

References 20 publications

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“…Queueing models can be subjected to mean-value analysis (MVA) [19] and more efficient variants thereof [4,7], which use the arrival theorem and Little's law to compute accurate estimates of average steady-state performance metrics (e.g. throughput, utilisation, and response time), many orders of magnitude faster than simulative approaches.…”

Section: Introductionmentioning

confidence: 99%

Relating layered queueing networks and process algebra models

Tribastone

2010

Proceedings of the First Joint WOSP/SIPEW International Conference on Performance Engineering

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This paper presents a process-algebraic interpretation of the Layered Queueing Network model. The semantics of layered multi-class servers, resource contention, multiplicity of threads and processors are mapped into a model described in the stochastic process algebra PEPA. The accuracy of the translation is validated through a case study of a distributed computer system and the numerical results are used to discuss the relative strengths and weaknesses of the different forms of analysis available in both approaches, i.e., simulation, mean-value analysis, and differential approximation.

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Section: Introductionmentioning

confidence: 99%

Relating layered queueing networks and process algebra models

Tribastone

2010

Proceedings of the First Joint WOSP/SIPEW International Conference on Performance Engineering

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“…Many Approximate Mean Value Analysis (AMVA) algorithms have been proposed for product-form queueing networks [3,6,7,11,14,16,18,31,32,[34][35][36]38,40,41]. These algorithms tend to yield relatively accurate solutions for product-form queueing networks with reduced computational requirements.…”

Section: Introductionmentioning

confidence: 99%

“…Approximate MVA (AMVA) algorithms [3,6,7,11,16,18,31,32,[34][35][36]38,40,41] reduce the time and space complexities by substituting approximations for A (c) k ( N ) that do not depend on the performance measures of lower population levels. Most AMVA algorithms involve iteratively solving a set of nonlinear equations using numerical techniques such as successive substitution or some variant of Newton's method [26,41].…”

Section: Introductionmentioning

confidence: 99%

The general form linearizer algorithms: A new family of approximate mean value analysis algorithms

Wang

Sevcik

Serazzi

et al. 2008

Performance Evaluation

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“…For the same reason, the approximate solution methods which have been shown empirically to have good accuracy [1,3,4,14,16,17,22] [4,16,22]. (It was shown recently by de Souza e Silva and Muntz [18] that a single iteration of Linearizer can be implemented with a computation time of O(MK 2) instead of O(MK 3) as indicated by Chandy and Neuse [4].)…”

Section: Introductionmentioning

confidence: 99%

PAM-a noniterative approximate solution method for closed multichain queueing networks

Hsieh¹,

Lam²

1988

Proceedings of the 1988 ACM SIGMETRICS Conference on Measurement and Modeling of Computer Systems

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Approximate MVA algorithms for separable queueing networks are based upon an iterative solution of a set of modified MVA formulas. Although each iteration has a computational time requirement of O(MK 2) or less, many iterations are typically needed for convergence to a solution. (M denotes the number of queues and K the number of closed chains or customer classes.) We present some faster approximate solution algorithms that are noniterative. They are suitable for the analysis and design of communication networks which may require tens to hundreds, perhaps thousands, of closed chains to model flow-controlled virtual channels. Three PAM algorithms of increasing accuracy are presented. Two of them have time and space requirements of O(MK). The third algorithm has a time requirement of O(MK 2) and a space requirement of O(MK).

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A Clustering Approximation Technique for Queueing Network Models with a Large Number of Chains

Cited by 19 publications

References 20 publications

Relating layered queueing networks and process algebra models

Relating layered queueing networks and process algebra models

The general form linearizer algorithms: A new family of approximate mean value analysis algorithms

PAM-a noniterative approximate solution method for closed multichain queueing networks

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