Experiments with Improved Approximate Mean Value Analysis Algorithms

Abstract

Approximate Mean Value Analysis (MVA) is a popular technique for analyzing queueing networks because of the efficiency and accuracy that it affords. In this paper, we present a new software package, called the Improved Approximate Mean Value Analysis Library (IAMVAL), which can be easily integrated into existing commercial and research queueing network analysis packages. The IAMVAL packages includes two new approximate MVA algorithms, the Queue Line (QL) algorithm and the Fraction Line (FL) algorithm, for analyzing multiple class separable queueing networks. The QL algorithm is always more accurate than, and yet has approximately the same computational efficiency as, the Bard-Schweitzer Proportional Estimation (PE) algorithm, which is currently the most widely used approximate MVA algorithm. The FL algorithm has the same computational cost and, in noncongested separable queueing networks where queue lengths are quite small, yields more accurate solutions than both the QL and PE algorithms.KeywordsQueue LengthFraction LineSingle ClassMultiple ClassQueueing NetworkThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.