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Suitable estimators for a class of Large Deviation approximations of rareevent probabilities based on sample realizations of random processes have beenproposed in our earlier work. These estimators are expressed as non-linearmulti-dimensional optimization problems of a special structure. In this paper,we develop an algorithm to solve these optimization problems very efficientlybased on their characteristic structure. After discussing the nature of theobjective function and constraint set and their peculiarities, we provide aformal proof that the developed algorithm is guaranteed to always converge. Theexistence of efficient and provably convergent algorithms for solving theseproblems is a prerequisite for using the proposed estimators in real timeproblems such as call admission control, adaptive modulation and coding withQoS constraints, and traffic anomaly detection in high data rate communicationnetworks.

Robust measurement-based buffer overflow probability estimators for QoS provisioning and traffic anomaly prediction applicationm