SABES: Statistical Available Bandwidth EStimation from passive TCP measurements

Estimating available network resources is fundamental when adapting the sending rate both at the application and transport layer. Traditional approaches either rely on active probing techniques or iteratively adapting the average sending rate, as is the case for modern TCP congestion control algorithms. In this paper, we propose a statistical method based on the inter-packet arrival time analysis of TCP acknowledgments to estimate a path available bandwidth. SABES first estimates the bottleneck link capacity exploiting the TCP flow slow start traffic patterns. Then, an heuristic based on the capacity estimation, provides an approximation of the end-to-end available bandwidth. Exhaustive experimentation on both simulations and real-world scenarios were conducted to validate our technique, and our results are promising. Furthermore, we train an artificial neural network to improve the estimation accuracy.