Simulating Network Paths with Recurrent Buffering Units
Divyam Anshumaan, Sriram Balasubramanian, Shubham Tiwari, Nagarajan Natarajan, Sundararajan Sellamanickam, Venkat N. Padmanabhan
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.
BibTeX
@article{Anshumaan_Balasubramanian_Tiwari_Natarajan_Sellamanickam_Padmanabhan_2023, title={Simulating Network Paths with Recurrent Buffering Units}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/25820}, DOI={10.1609/aaai.v37i6.25820}, abstractNote={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.}, number={6}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Anshumaan, Divyam and Balasubramanian, Sriram and Tiwari, Shubham and Natarajan, Nagarajan and Sellamanickam, Sundararajan and Padmanabhan, Venkat N.}, year={2023}, month={Jun.}, pages={6684-6692} }