In high-speed networks, it is important to detect the presence of large flows—also known as elephant flows— because of their adverse effects on delay-sensitive flows. If detected on a timely fashion, network operators can apply active policies such as flow redirection or traffic shaping to ensure the overall quality of service of the network is preserved. Towards this objective, we develop a high-performance data structure and algorithm to address the problem of detecting large flows at very high-speed rates. Our solution leverages the concept of optimal sampling rate under partial information to avoid the need for processing every single packet on the network. With this strategy, we present a prototype of a high-performance network sensor capable of processing traffic rates at 100Gbps and detect the largest flows with high accuracy.
For information on Reservoir’s technology related to this paper, visit GradientGraph.