The Queueing Network Analyzer

@article{Whitt1983TheQN,
  title={The Queueing Network Analyzer},
  author={Ward Whitt},
  journal={The Bell System Technical Journal},
  year={1983},
  volume={62},
  pages={2779-2815}
}
  • W. Whitt
  • Published 1 November 1983
  • Computer Science
  • The Bell System Technical Journal
This paper describes the Queueing Network Analyzer (QNA), a software package developed at Bell Laboratories to calculate approximate congestion measures for a network of queues. The first version of QNA analyzes open networks of multiserver nodes with the first-come, first-served discipline and no capacity constraints. An important feature is that the external arrival processes need not be Poisson and the service-time distributions need not be exponential. Treating other kinds of variability is… 

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References

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Performance of the Queueing Network Analyzer
  • W. Whitt
  • Mathematics
    The Bell System Technical Journal
  • 1983
TLDR
The examples here demonstrate the importance of the variability parameters used in QNA to describe non-Poisson arrival processes and nonexponential service-time distributions and show that QNA performs much better than the standard Markovian algorithm, which does not use variability parameters.
Departures from a Queue with Many Busy Servers
  • W. Whitt
  • Mathematics
    Math. Oper. Res.
  • 1984
TLDR
This paper shows that the stationary departure process is approximately Poisson when there are many busy slow servers in a large class of stationary G/GI/s congestion models having s servers, infinite waiting room, the first-come first-served discipline, and mutually independent and identically distributed service times that are independent of a stationary arrival process.
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    The Bell System Technical Journal
  • 1982
TLDR
A class of approximations for the waiting time distribution in single server queueing systems with general independent (renewal) input and general (independent) service time distributions is presented.
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TLDR
This work calculates the set of possible values for the mean queue length in a GI/M/1 queue and shows how it depends on the traffic intensity and the second moment, and uses extremal distributions to compare alternative parameters for approximations.
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TLDR
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