Simulating Network Paths with Recurrent Buffering Units

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.

Authors

• Divyam Anshumaan Microsoft Research India
• Sriram Balasubramanian University of Maryland, College Park Microsoft Research India
• Shubham Tiwari Microsoft Research India
• Nagarajan Natarajan Microsoft Research India
• Sundararajan Sellamanickam Microsoft Research India
• Venkat N. Padmanabhan Microsoft Research India

Keywords

• APP: Communication
• APP: Web
• ML: Applications
• ML: Deep Generative Models & Autoencoders
• ML: Time-Series/Data Streams