Network Delay Measurement with Machine Learning: From Lab to Real-World Deployment

Abstract

Artificial Intelligence (AI) continues to impact all facets of technology including Instrumentation and Measurement (I&M) with much effort spent on developing I&M systems assisted by machine learning (ML), especially deep learning [1]. While these ML-assisted I&M systems show promising results in a lab environment, there is always the question of how well they will perform in the real world. In fact, concerns about the real-world performance of ML is not exclusive to I&M but an inherent property of ML in general, because ML is data driven and its performance will change if the data distribution changes in the real world. In this article, we present a case study of developing in the lab an ML-assisted I&M system, specifically a network delay predictor, and deploying it in the real world, achieving 93% accuracy.