Simulating physical network paths (e.g., Internet) is a cornerstone research problem in the emerging sub-field of AI-for-networking. We seek a model that generates end-to-end packet delay values in response to the time-varying load offered by a sender, which is typically a function of the previously output delays. The problem setting is unique, and renders the state-of-the-art text and time-series generative models inapplicable or ineffective. We formulate an ML problem at the intersection of dynamical systems, sequential decision making, and time-series modeling. We propose a novel grey-box approach to network simulation that embeds the semantics of physical network path in a new RNN-style model called RBU, providing the interpretability of standard network simulator tools, the power of neural models, the efficiency of SGD-based techniques for learning, and yielding promising results on synthetic and real-world network traces.
@article{arxiv.2202.13870,
title = {Simulating Network Paths with Recurrent Buffering Units},
author = {Divyam Anshumaan and Sriram Balasubramanian and Shubham Tiwari and Nagarajan Natarajan and Sundararajan Sellamanickam and Venkata N. Padmanabhan},
journal= {arXiv preprint arXiv:2202.13870},
year = {2022}
}