The QNET method for two-moment analysis of open queueing networks
Queueing Systems: Theory and Applications
Arranging queues in series: simulation experiment
Management Science
Correlation induction without the inverse transformation
WSC '86 Proceedings of the 18th conference on Winter simulation
Approximate Methods for Analyzing Queueing Network Models of Computing Systems
ACM Computing Surveys (CSUR)
Computer Communications Network Design and Analysis
Computer Communications Network Design and Analysis
Approximate analysis of M/G/c/c state-dependent queueing networks
Computers and Operations Research
Queueing Systems: Theory and Applications
A hybrid analysis method for multi-class queueing networks with multi-server nodes
Decision Support Systems
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In this paper we describe a new variation on QNA, the Queueing Network Analyzer (Whitt [38]). QNA calculates approximate congestion measures for a network of queues. The variant proposed here allows us to relax QNA's assumption that certain counts of events in the network are uncorrelated. Following QNA, the network is approximated in terms of three basic operations on streams, namely, queueing, splitting and superposition, and random quantities of interest are characterized approximately by two moments. The approach differs from QNA in that instead of expressing the network operations as transformations applied to two-moment characterizations of the inputs, it expresses these operations as regression relations between counts in the output streams and the input streams. In essence, we substitute approximate regression relationships between random variables for QNA's approximate moment relationships. This device allows us to enhance QNA's moment relationships with information about correlations between the streams. We compare the performance of the proposed approach with QNA on a simple feed-forward network to demonstrate the need for taking account of correlation. We also discuss some of the limitations of this approach, particularly its difficulty in handling feedback, and give some ideas to improve accuracy.