Computation and optimization methods for multiresource queues
type: Item
instance of: scholarly article
title: Computation and optimization methods for multiresource queues
zbMATH Open document ID: 0898.60094
DOI: 10.1007/BF02366862
author: A. Z. Melikov
published in: Cybernetics and Systems Analysis
publication date: 1998-11-01T00:00:00Z
review text: The operation of a large class of complex cybernetic systems that process and transmit information is described with fair accuracy by models of multistream queueing systems. The need to use multistream models emerges with particular clarity in the context of digital integral-service queueing networks, where a single network transmits different kinds of information: speech, video, fax, etc. Yet the modeling of stochastic processes describable by multistream queues depends on various assumptions. The main assumption is basic for the classical queueing theory: a customer from any of the streams uses at most one server (resource) at any time. In many real queueing systems, however, this assumption is not satisfied. For such systems \textit{K. J. Omahen} [J. Assoc. Comput. Machin. 24, 646-663 (1977; Zbl 0401.68014)] introduced the notion of multiresource queues. A multiresource queue (MRQ) is a multiserver system in which customers of different types request a random number of servers simultaneously. MRQ models are highly relevant: on the one hand, they constitute a generalization of classical queueing models, and on the other hand they provide a fairly precise description of the operation of various complex cybernetic systems. We distinguish two types of MRQ, depending on the strategy by which an arriving customer captures servers, and the fate of the server that has completed its ``share'' of serving the customer. These two types are characterized as follows [see \textit{L. A. Ponomarenko} and the author, Elektron. Modelirov. 11, No. 2, 67-70 (1989) and \textit{Y. de Serre} and \textit{L. G. Mason}, IEEE Trans. Commun. 36, No. 6, 675-684 (1988)]: 1) Servers requested by one customer start processing simultaneously, and the server that has completed its share of processing is blocked until the last server in the group finishes its operation. 2) Different servers requested by one customer do not necessarily start at the same time, and each server is assigned an individua…
Mathematics Subject Classification ID: 60K25
Mathematics Subject Classification ID: 90B22
zbMATH DE Number: 1147449
zbMATH Keywords: multistream models
zbMATH Keywords: queueing theory
zbMATH Keywords: random number of servers
MaRDI profile type: Publication
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full work available at URL: https://doi.org/10.1007/bf02366862
OpenAlex ID: W2076466317
description: scientific article; zbMATH DE number 1147449