Akaike information criterion statistics
Akaike information criterion statistics
Effective bandwidths at multi-class queues
Queueing Systems: Theory and Applications
Effective bandwidths for the multi-type UAS channel
Queueing Systems: Theory and Applications
A framework for robust measurement-based admission control
IEEE/ACM Transactions on Networking (TON)
Class-specific quality of service guarantees in multimedia communication networks
Automatica (Journal of IFAC)
On the estimation of buffer overflow probabilities from measurements
IEEE Transactions on Information Theory
Modeling full-length VBR video using Markov-renewal-modulated TES models
IEEE Journal on Selected Areas in Communications
Modeling video traffic using M/G/∞ input processes: a compromise between Markovian and LRD models
IEEE Journal on Selected Areas in Communications
An introduction to large deviations for communication networks
IEEE Journal on Selected Areas in Communications
Entropy of ATM traffic streams: a tool for estimating QoS parameters
IEEE Journal on Selected Areas in Communications
Admission control for statistical QoS: theory and practice
IEEE Network: The Magazine of Global Internetworking
An efficient technique to analyze the impact of bursty TCP traffic in wide-area networks
Performance Evaluation
Spatio-temporal network anomaly detection by assessing deviations of empirical measures
IEEE/ACM Transactions on Networking (TON)
The Journal of Supercomputing
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We consider the problem of estimating buffer overflow probabilities when the statistics of the input traffic are not known and have to be estimated from measurements. We start by investigating the use of Markov-modulated processes in modeling the input traffic and propose a method for selecting an optimal model based on Akaike's Information Criterion. We then consider a queue fed by such a Markov-modulated input process and use large deviations asymptotics to obtain the buffer overflow probability. The expression for this probability is affected by estimation errors in the parameters of the input model. We analyze the effect of these errors and propose a new, more robust, estimator which is less likely to underestimate the overflow probability than the estimator obtained by certainty equivalence. As such, it is appropriate in situations where the overflow probability is associated with Quality of Service (QoS) and we need to provide firm QoS guarantees. Nevertheless, as the number of observations increases, the proposed estimator converges with probability 1 to the appropriate target, and thus, does not lead to resource underutilization in this limit.