A Foundation for Stochastic Bandwidth Estimation of Networks with Random Service

TL;DR

This paper introduces a stochastic framework for network bandwidth estimation using max-plus algebra, enabling adaptive probing strategies to accurately infer service bounds amid variable cross traffic.

Contribution

It develops a novel estimation methodology based on iterative constant rate probes and stochastic max-plus algebra, improving bandwidth inference in networks with random service.

Findings

Effective bandwidth estimation with adaptive probe parameters.

Demonstrated robustness against cross traffic variability.

Comparison shows advantages over existing tools.

Abstract

We develop a stochastic foundation for bandwidth estimation of networks with random service, where bandwidth availability is expressed in terms of bounding functions with a defined violation probability. Exploiting properties of a stochastic max-plus algebra and system theory, the task of bandwidth estimation is formulated as inferring an unknown bounding function from measurements of probing traffic. We derive an estimation methodology that is based on iterative constant rate probes. Our solution provides evidence for the utility of packet trains for bandwidth estimation in the presence of variable cross traffic. Taking advantage of statistical methods, we show how our estimation method can be realized in practice, with adaptive train lengths of probe packets, probing rates, and replicated measurements required to achieve both high accuracy and confidence levels. We evaluate our method in a controlled testbed network, where we show the impact of cross traffic variability on the time-scales of service availability, and provide a comparison with existing bandwidth estimation tools.