A machine learning-based approach for estimating available bandwidth

In this paper, we propose a machine learning-based approach for estimating available bandwidth. We evaluate the approach via simulations using two probing models: a packet train probing model and a pathChirp-like probing model. The simulation results show that the former cannot yield accurate estimates in our system; however, using the pathChirp-like probing model, the proposed approach can estimate the available bandwidth with moderate traffic overhead more accurately than two widely used tools, pathChirp and Spruce. Moreover, we propose a normalization method that improves our approach's ability to estimate available bandwidth, even if there are no samples with similar properties to the measured path in the training dataset. The effectiveness and simplicity of this novel approach make it a promising scheme that goes a long way toward achieving accurate estimation of available bandwidth on Internet paths.