2022/02/23 by Divyam Anshumaan, Sriram Balasubramanian, Anshumaan, Divyam +9 · 1 citation
Decision Sciences · Computer Science · Engineering · #Simulation Techniques and Applications #Seismology and Earthquake Studies #Traffic Prediction and Management Techniques
paper · pdf · doi:10.48550/arxiv.2202.13870
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.