Coarse Estimation of Bottleneck Router’s Buffer Size for Heterogeneous TCP Sources
Christian Vega Caicedo, Jorge E. E. Pezoa, Elie F. Kfoury, Jorge Crichigno · 2021
Although the importance of router's buffer sizing in network performance is well known, estimating the current size of the bottleneck buffer is an open research problem. This paper presents a method to achieve such estimation, for the case where the bottleneck buffer operates under a finite number of buffer sizing regimes. The scheme uses a supervised machine learning approach to properly model such regimes and a classification mechanism to predict the coarse buffer size using the following end-to-end network measurements, which are collected at the sender side: throughput, Round Trip Time (RTT), and Congestion Window (CWND). In contrast to previous work, the scheme does not assume a homogeneous congestion control algorithm used by the senders. The proposed approach was tested using data collected on a real testbed. The corresponding results show that the Support Vector Machine (SVM) Radial Basis Function (RBF) classifier correctly estimates the bottleneck buffer size, under different network conditions.