CRII: NeTS: Denoising Internet Delay Measurements using Weak Supervision

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Understanding the characteristics of the Internet is one of the key goals of Internet measurement researchers, and the service providers and content delivery networks that serve billions of users worldwide. To this end, a myriad of measurement tools and techniques have been developed. Despite these efforts, what is critically lacking is a systematic framework to interpret the results due to the presence of measurement noise. This research proposes to develop a new framework to solve this problem. The main goal of this project is to design, develop and rigorously evaluate a framework for denoising latency measurements. The objectives are to make the denoising process easy, automatic, and rapid via two key research thrusts. In the first thrust, the goal is to design and develop the framework to generate measurement noise labels (i.e., ground truth data) automatically leveraging recent advancements in machine learning. The second thrust will expand the capabilities of the framework to remove and repair the noisy measurements in an automated and rapid fashion. The efficacy of the framework will be evaluated in lab-based settings and in a real-world setting by applying it on community datasets.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.