Inferring Queue Performance from Transactional Data

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Congestion as occurs in waiting lines or queues (as found in telecommunications, retailing, ATM's, etc) is the focus of this research proposal. The proposal builds on a new statistical inference method -- "Queue Inference Engine (QIE)" -- that computes statistically unbiased estimates of virtually all relevant queue performance measures from the transactional data alone, with no queue observation or parameter estimation required. The project builds on earlier work in four ways (1) to derive various bounds and approximations that speed computation and facilitate analysis of very congested systems; (2) to create new queue inference algorithms in more complex settings; (3) to derive the equivalent of statistical confidence limits on the QIE results, so that one can know a priori required sample sizes; and (4) to attempt to create a server allocation or scheduling algorithm within the QIE framework, that would be data intensive, essentially parameter free and would reflect accurately transient operations.