Taking account of correlations between streams in queueing network approximations
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 QNAs approximate moment relationships. This device allows us to enhance QNAs 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.
queueing networks qna decomposition method of innovations point process