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

TL;DR

This paper introduces an efficient, convergent algorithm for estimating rare event probabilities in network traffic, enabling real-time QoS management and anomaly detection.

Contribution

It develops a specialized, provably convergent algorithm to solve complex optimization problems for large deviation-based estimators in real-time network applications.

Findings

Algorithm guarantees convergence for optimization problems

Enables real-time estimation of rare event probabilities

Applicable to QoS and traffic anomaly detection

Abstract

Suitable estimators for a class of Large Deviation approximations of rare event probabilities based on sample realizations of random processes have been proposed in our earlier work. These estimators are expressed as non-linear multi-dimensional optimization problems of a special structure. In this paper, we develop an algorithm to solve these optimization problems very efficiently based on their characteristic structure. After discussing the nature of the objective function and constraint set and their peculiarities, we provide a formal proof that the developed algorithm is guaranteed to always converge. The existence of efficient and provably convergent algorithms for solving these problems is a prerequisite for using the proposed estimators in real time problems such as call admission control, adaptive modulation and coding with QoS constraints, and traffic anomaly detection in high data rate communication networks.