queuing network model


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queuing network model

[′kyü·iŋ ‚net‚wərk ‚mäd·əl]
(computer science)
A model that represents a computer system by a network of devices through which customers (such as transactions, processes, or server requests) flow, and queues may form at each device due to its finite service rate.
References in periodicals archive ?
LMINET [5] and LMINET2 [6] are national queuing network models that model the airports as M(t)/[E.sub.k](t)/1 queues.
Table 2 General Queuing Network Models Source Queuing Environment Description Jackson [1963] Poisson arrival, Under the product--form exponential service structure, the system is times, multi--stages, solved by analyzing each FCFS, open queuing node separately and then network, exact analysis the results are combined.
For the proposed simulation model, we used both queuing network model with the varying parameter's network topology and mathematical equations derived in our earlier work.
discussed open queuing network models with single and multiple servers [13].
This technique is based on multi-class product form queuing network models and the mean value analysis algorithm (Basket et al., 1975; Reiser and Lavenberg, 1980).
Multi-class product form queuing network models have been widely used in performance analysis of resource sharing systems, such as computer systems and communication networks (Bolch et al., 2006).
In the analytical model, an open queuing network model (QNM) is proposed, which consists of five interacting multiclass generalized exponential (GE)-type queuing and delay systems with multiple servers and finite capacities.
Using GE-type distributions and related queuing models can help achieve performance measurements (utilization, mean queue length, and blocking probability etc) with a complex queuing network model. On the other hand, Maximum Entropy (ME) is a key solution to the problems of modeling an unlimited queuing network with a GE-type distribution, and the ME analytic solution obtained in our paper may apply to other traffic distributions (e.g., self-similar and long range distributions) with proper modifications.