FABMon: Enabling Fast and Accurate Network Available Bandwidth Estimation

Abstract

Characterizing the end-to-end network available bandwidth (ABW) is an important but challenging task. Although a number of ABW estimation tools have been introduced over the past two decades, applying them to the real-world networks is still difficult because of the biased results, heavy load, and long measurement time. In this paper, we propose a novel Burst Queue Recovery (BQR) model to infer the ABW. BQR first induces an instant network congestion and then observes the one-way delay (OWD) variation until the tight link recovers from the congestion. By correlating the OWDs with the queue length variation, BQR can calculate the ABW accurately. Compared to the traditional probe gap model (PGM) and probe rate model (PRM), our theoretical analysis and simulations show that BQR is more tolerant to the transient traffic burst and supports the scenarios with multiple congestible links. Based on the model, we build FABMon, a fast and accurate ABW estimation tool. Our experiments show that FABMon can measure ABW within 50 milliseconds, and achieve much more accurate measurement results than the existing tools with a very small volume of probe packets.