In:
Journal of the ACM, Association for Computing Machinery (ACM), Vol. 41, No. 6 ( 1994-11), p. 1110-1135
Abstract:
Although many closed multiclass queuing networks have a product-form solution, evaluating their performance measures remains nontrivial due to the presence of a normalization constant. We propose the application of Monte Carlo summation in order to determine the normalization constant, throughputs, and gradients of throughputs. A class of importance-sampling functions leads to a decomposition approach, where separate single-class problems are first solved in a setup module, and then the original problem is solved by aggregating the single-class solutions in an execution model. We also consider Monte Carlo methods for evaluating performance measures based on integral representations of the normalization constant; a theory for optimal importance sampling is developed. Computational examples are given that illustrate that the Monte Carlo methods are robust over a wide range of networks and can rapidly solve networks that cannot be handled by the techniques in the existing literature.
Type of Medium:
Online Resource
ISSN:
0004-5411
,
1557-735X
DOI:
10.1145/195613.195630
Language:
English
Publisher:
Association for Computing Machinery (ACM)
Publication Date:
1994
detail.hit.zdb_id:
2006500-0
detail.hit.zdb_id:
6759-3
