@inproceedings{anshumaan2023simulating,
author = {Anshumaan, Divyam and Balasubramanian, Sriram and Tiwari, Shubham and Natarajan, Nagarajan and Sellamanickam, Sundararajan and Padmanabhan, Venkat},
title = {Simulating Network Paths with Recurrent Buffering Units},
organization = {AAAI},
booktitle = {The 37th AAAI Conference on Artificial Intelligence},
year = {2023},
month = {February},
abstract = {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 Recurrent Buffering Unit, 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.},
url = {http://approjects.co.za/?big=en-us/research/publication/simulating-network-paths-with-recurrent-buffering-units/},
}