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. We formulate a novel ML problem at the intersection of dynamical systems, sequential decision making, and time-series generative modeling. The problem setting is unique, and renders the state-of-the-art neural generative modeling work in the text and time-series domains inapplicable, or ineffective. We propose a novel grey-box approach to network simulation that embeds the semantics of physical network path in a new RNN-style architecture 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.

Simulating Network Paths with Recurrent Buffering Units

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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Venkata Padmanabhan

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Simulating Network Paths with Recurrent Buffering Units

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