Multi-path Probabilistic Available Bandwidth Estimation through Bayesian Active Learning

TL;DR

This paper presents a Bayesian active learning method for efficiently estimating probabilistic available bandwidth across multiple network paths, reducing probe requirements while maintaining accuracy.

Contribution

It introduces a novel distributed algorithm leveraging probabilistic graphical models for multi-path bandwidth estimation using active learning techniques.

Findings

Significant reduction in the number of probes needed for accurate estimates.

Effective estimation of probabilistic available bandwidth in simulated and real network environments.

Utilization of shared link information improves estimation efficiency.

Abstract

Knowing the largest rate at which data can be sent on an end-to-end path such that the egress rate is equal to the ingress rate with high probability can be very practical when choosing transmission rates in video streaming or selecting peers in peer-to-peer applications. We introduce probabilistic available bandwidth, which is defined in terms of ingress rates and egress rates of traffic on a path, rather than in terms of capacity and utilization of the constituent links of the path like the standard available bandwidth metric. In this paper, we describe a distributed algorithm, based on a probabilistic graphical model and Bayesian active learning, for simultaneously estimating the probabilistic available bandwidth of multiple paths through a network. Our procedure exploits the fact that each packet train provides information not only about the path it traverses, but also about any path that shares a link with the monitored path. Simulations and PlanetLab experiments indicate that this process can dramatically reduce the number of probes required to generate accurate estimates.