FlowShark: Sampling for High Flow Visibility in SDNs

Abstract

As the scale and speed of modern networks continue to increase, traffic sampling has become an indispensable tool in network management. While there exist a plethora of sampling solutions, they either provide limited flow visibility or have poor scalability in large networks. This paper presents the design and evaluation of FlowShark, a high-visibility per-flow sampling system for Software-Defined Networks (SDNs). The key idea in FlowShark is to separate sampling decisions on short and long flows, whereby sampling short flows is managed locally on edge switches, while a central controller optimizes sampling decisions on long flows. To this end, we formulate flow sampling as an optimization problem and design an online algorithm with a bounded competitive ratio to solve the problem efficiently. To show the feasibility of our design, we have implemented FlowShark in a small OpenFlow network using Mininet. We present experimental results of our Mininet implementation as well as performance benchmarks obtained from packet-level simulations in larger networks. Our experiments with a machine learning based Traffic Classifier application show up to 27% and 19% higher classification recall and precision, respectively, with FlowShark compared to existing sampling approaches.