A Virtual Queue Approach for Online Estimation of Loss Probability Based on MVA Theory

TL;DR

This paper introduces a virtual queue method leveraging MVA theory to estimate small loss probabilities in network queues, enabling real-time, accurate performance monitoring without high computational costs.

Contribution

It presents a novel virtual queue scheme based on MVA theory that allows online estimation of loss probabilities with high accuracy and computational efficiency.

Findings

Accurately estimates small loss probabilities using virtual queues.

Retains MVA method accuracy for aggregated traffic.

Reduces computational complexity for real-time applications.

Abstract

In network quality of service provisioning, premium services generally require to keep a very small loss probability, which is infeasible to measure directly. The proposed virtual queue scheme estimates the small packet loss probability of a real queueing system by measuring queue statistics in a set of separate virtual queues. A novel scaling property between the real queue and the virtual queues is deduced on the basis of the maximum variance asymptotic (MVA) theory. The new scheme retains the high accuracy and wide applicability of the MVA method for aggregated traffic while avoiding the high computational complexity in a direct application of the original MVA analysis in real time. This makes it suitable for online measurement applications such as network performance monitoring and measurement-based admission control.