Internet Distance Prediction Using Node-Pair Geography

Abstract

Predictive methods for learning network distances are often more desirable than direct performance measurements between end hosts. Yet, predicting network distances remains an open and difficult problem, as the results from a number of comparative and analytical studies have shown. From an application requirements perspective, there is significant room for improvement in achieving prediction accuracies at a satisfactory level. In this paper, we develop and analyze a new, machine learning-based approach to distance prediction that seeks to capture and generalize geographical characteristics between Internet node pairs, instead of relying on direct and ongoing measurements of partial paths. We apply a basic algorithm in machine learning to demonstrate this idea and highlight the potential benefits that this method may offer over other popular methods that exist today.