Abstract: Given the growing significance of network performance, it is crucial to examine how to make the most of available network options and protocols. We propose ECON, a model that predicts performance of applications under different protocols and network conditions to scalably make better network choices. ECON is built on an analytical framework to predict TCP performance, and uses the TCP model as a building block for predicting application performance. ECON infers a relationship between loss and congestion using empirical data that drives an online model to predict TCP performance. ECON then builds on the TCP model to predict latency and HTTP performance. Across four wired and one wireless network, our model outperforms seven alternative TCP models. We demonstrate how ECON (i) can be used by a Web server application to choose between HTTP/1.1 and HTTP/2 for a given Web page and network condition, and (ii) can be used by a video application to choose the optimal bitrate that maximizes video quality without rebuffering. Time permitting, we will discuss how ECON may apply to modern congestion control algorithms, like BBR.
Bio: Anshul Gandhi is an Associate Professor in the Computer Science Department at Stony Brook University. He received his Ph.D. from Carnegie Mellon University in 2013 and then spent a year as a postdoc at the IBM T. J. Watson Research Center. His current research focuses on performance modeling in distributed systems, and is funded by an NSF Career award, an IBM Faculty award, and a Google Research award. His contributions to performance modeling were recently recognized by an ACM Sigmetrics Rising Star Award.
