Prophet: Fast Accurate Model-Based Throughput Prediction for Reactive Flow in DC Networks

As modern network applications (e.g., large data analytics) become more distributed and can conduct application-layer traffic adaptation, they demand better network visibility to better orchestrate their data flows. As a result, the ability to predict the available bandwidth for a set of flows has become a fundamental requirement of today's networking systems. While there are previous studies addressing the case of non-reactive flows, the prediction for reactive flows, e.g., flows managed by TCP congestion control algorithms, still remains an open problem. In this paper, we identify three challenges in providing throughput prediction for reactive flows: throughput dynamics, heterogeneous reactive control mechanisms, and source-constrained flows. Based on a previous theoretical model, we introduce a novel learning-based prediction system with a key component named fast factor learning (FFL) model. We adopt novel techniques to overcome practical concerns such as scalability, convergence and unknown system parameters. A system, Prophet, is proposed leveraging the emerging technologies of Software Defined Networking (SDN) to realize the model. Evaluations demonstrate that our solution achieves significant accuracy in a wide range of settings.