Real-Time Multi-path Tracking of Probabilistic Available Bandwidth

TL;DR

This paper introduces a real-time probabilistic bandwidth tracking method using Bayesian inference, belief propagation, and particle filters, demonstrated on PlanetLab to improve network throughput estimation with minimal overhead.

Contribution

It presents a novel framework combining chirps, Bayesian inference, and active sampling for real-time probabilistic bandwidth estimation and tracking across multiple network paths.

Findings

Outperforms block-based algorithms in input rate efficiency

Achieves higher probability of successful transmission

Successfully deployed on PlanetLab for online experiments

Abstract

Applications such as traffic engineering and network provisioning can greatly benefit from knowing, in real time, what is the largest input rate at which it is possible to transmit on a given path without causing congestion. We consider a probabilistic formulation for available bandwidth where the user specifies the probability of achieving an output rate almost as large as the input rate. We are interested in estimating and tracking the network-wide probabilistic available bandwidth (PAB) on multiple paths simultaneously with minimal overhead on the network. We propose a novel framework based on chirps, Bayesian inference, belief propagation and active sampling to estimate the PAB. We also consider the time evolution of the PAB by forming a dynamic model and designing a tracking algorithm based on particle filters. We implement our method in a lightweight and practical tool that has been deployed on the PlanetLab network to do online experiments. We show through these experiments and simulations that our approach outperforms block-based algorithms in terms of input rate cost and probability of successful transmission.