on the convolution algorithm for separable queuing networks
Research into queuing networks and their applications to computer systems is in a state of prosperity. The object of this paper is to discuss the computational aspect of separable queuing networks. Separable networks constitute that class of models for which a solution can be computed efficiently for fairly large problems. Open networks do not pose any computational problem. It is the case of closed networks where the subject of numerical algorithms becomes an issue. In this paper, we shall take a fresh look at closed queuing networks, which we introduce as conditioned solution of suitably chosen open networks. This view will provide a probabilistic interpretation of what is normally called the normalization constant. Computational algorithms, then, result in a systematic way.
algorithm network applications computation systems object paper aspect class model case interpretation normalization