Deducing queueing from transactional data: the queue inference engine, revisited
Operations Research - Supplement to Operations Research: stochastic processes
Estimating traffic parameters in queueing systems with local information
Performance Evaluation
A central-limit-theorem-based approach for analyzing queue behavior in high-speed networks
IEEE/ACM Transactions on Networking (TON)
A queueing system with discrete autoregressive arrivals
Performance Evaluation
A Tandem Queueing Model for Delay Analysis in Disconnected Ad Hoc Networks
ASMTA '08 Proceedings of the 15th international conference on Analytical and Stochastic Modeling Techniques and Applications
A traffic based decomposition of two-class queueing networks with priority service
Computer Networks: The International Journal of Computer and Telecommunications Networking
Reconstructing arrival processes to G/D/1 queueing systems and tandem networks
SPECTS'09 Proceedings of the 12th international conference on Symposium on Performance Evaluation of Computer & Telecommunication Systems
Queueing networks with discrete time scale: explicit expressions for the steady state behavior of discrete time stochastic networks
GSM phase 2+ general packet radio service GPRS: Architecture, protocols, and air interface
IEEE Communications Surveys & Tutorials
A Cross-Layer Approach for WLAN Voice Capacity Planning
IEEE Journal on Selected Areas in Communications
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In this article we propose methods to estimate the parameters of arrival processes to G/D/1 queueing systems only based on observed departures from the system. The derived estimates can be used for performance evaluation and capacity planning in cases where the arrival process is not observable directly. Instead, only the departure process modified at the (last) server needs to be observed. Concerning the arrival process we begin by focusing on the normally distributed number of arrivals per interval and on autoregressive processes. Both classes can be used to model traffic in communication networks on links with a high degree of aggregation. In the case of autoregressive processes we apply the Tobit regression model in order to derive accurate estimates of all parameters of the arrival process. We then continue by generalizing the estimation procedures to correlated arrival processes by using the Buckley-James Estimator. The results are presented for the single G/D/1 queue but are then generalized to the more realistic scenario of sequences of queues with possibly varying bottleneck capacity. The latter especially permits the modeling of effects due to interfering cross traffic. We show how the derived estimates can be used for performance evaluation or capacity planning based on effective bandwidth theory. Finally, we demonstrate that the estimation procedures can be utilized within wireless networks by means of a detailed simulation model.