About: Jackson network is a research topic. Over the lifetime, 385 publications have been published within this topic receiving 14341 citations. The topic is also known as: Jacksonian network.
TL;DR: Many of the network results of Jackson on arrival and service rate dependencies, of Posner and Bernholtz on different classes of customers, and of Chandy on different types of service centers are combined and extended in this paper.
Abstract: We derive the joint equilibrium distribution of queue sizes in a network of queues containing N service centers and R classes of customers. The equilibrium state probabilities have the general form: P(S) - Cd(S) $f_1$($x_1$)$f_2$($x_2$)...$f_N$($x_N$) where S is the state of the system, $x_i$ is the configuration of customers at the ith service center, d(S) is a function of the state of the model, $f_i$ is a function that depends on the type of the ith service center, and C is a normalizing constant. We consider four types of service centers to model central processors, data channels, terminals, and routing delays. The queueing disciplines associated with these service centers include first-come-first-served, processor sharing, no queueing, and last-come-first-served. Each customer belongs to a single class of customers while awaiting or receiving service at a service center but may change classes and service centers according to fixed probabilities at the completion of a service request. For open networks we consider state dependent arrival processes. Closed networks are those with no arrivals. A network may be closed with respect to some classes of customers and open with respect to other classes of customers. At three of the four types of service centers, the service times of customers are governed by probability distributions having rational Laplace transforms, different classes of customers having different distributions. At first-come-first-served type service centers the service time distribution must be identical and exponential for all classes of customers. Many of the network results of Jackson on arrival and service rate dependencies, of Posner and Bernholtz on different classes of customers, and of Chandy on different types of service centers are combined and extended in this paper. The results become special cases of the model presented here. An example shows how different classes of customers can affect models of computer systems. Finally, we show that an equivalent model encompassing all of the results involves only classes of customers with identical exponentially distributed service times. All of the other structure of the first model can be absorbed into the fixed probabilities governing the change of class and change of service center of each class of customers.
TL;DR: What is perhaps the simplest theoretical question related to this class of problems, and derives expressions for certain steady-state parameters for discrete, statistically varying flows through complex networks.
Abstract: One of the least understood classes of operations problems is that concerned with the design, loading, and, especially, the scheduling of discrete, statistically varying flows through complex networks. The present paper abstracts what is perhaps the simplest theoretical question related to this class of problems, and derives expressions for certain steady-state parameters.
TL;DR: The equilibrium joint probability distribution of queue lengths is obtained for a broad class of jobshop-like "networks of waiting lines," where the mean arrival rate of customers depends almost arbitrarily upon the number already present, and the mean service rate at each service center depends almost arbitrary upon the queue length there.
Abstract: (This article originally appeared in Management Science, November 1963, Volume 10, Number 1, pp. 131-142, published by The Institute of Management Sciences.)
The equilibrium joint probability distribution of queue lengths is obtained for a broad class of jobshop-like "networks of waiting lines," where the mean arrival rate of customers depends almost arbitrarily upon the number already present, and the mean service rate at each service center depends almost arbitrarily upon the queue length there. This extension of the author's earlier work is motivated by the observation that real production systems are usually subject to influences which make for increased stability by tending, as the amount of work-in-process grows, to reduce the rate at which new work is injected or to increase the rate at which processing takes place.
TL;DR: A comparison of G-Networks with Multiple Classes of Signals and Positive Customers and Discrete-Time Queueing Systems shows how different approaches to queue management can produce different results.
Abstract: Queues with a Single Server. Jackson Networks. Extensions to Queues with a Single Server. Baskett, Chandy, Muntz and Palacios Networks. Approximate Methods. Flows in Networks. G-Networks: Positive and Negative Customers, Signals and Batch Removal. G-Networks with Multiple Classes of Signals and Positive Customers. Discrete-Time Queueing Systems. Bibliography. Appendix. Index.
TL;DR: Methods are presented for computing the equilibrium distribution of customers in closed queueing networks with exponential servers based on two-dimensional iterative techniques which are highly efficient and quite simple to implement.
Abstract: Methods are presented for computing the equilibrium distribution of customers in closed queueing networks with exponential servers. Expressions for various marginal distributions are also derived. The computational algorithms are based on two-dimensional iterative techniques which are highly efficient and quite simple to implement. Implementation considerations such as storage allocation strategies and order of evaluation are examined in some detail.