A Machine Learning Approach to Peer Connectivity Estimation for Reliable Blockchain Networking

Abstract

Peer connectivity plays a significant role in a blockchain network since any poor connectivity may result in the nodes operating on outdated data (e.g., cryptocurrency transactions). Although connectivity information is maintained by individual nodes, such identifier-based information might be unreliable due to the possibility of bogus identifiers. This paper tackles the problem of peer connectivity estimation through datadriven analytics of blockchain traffic for reliable blockchain networking. We define a set of variables to represent traffic characteristics and estimate peer connectivity from the collected data using a machine learning methodology. We also investigate the feasibility of feature prioritization to minimize estimation complexities. Our experimental results show that the presented estimation mechanism makes accurate predictions, with less than 0.1 difference between the measurement and estimation for over 99.7% of predictions. The time complexity measured on a commodity machine shows a microsecond scale for completing a single prediction task, enabling real-time operations.