Elephant Flow Detection and Load-Balanced Routing with Efficient Sampling and Classification

Feilong Tang, Heteng Zhang, Laurence T. Yang, Long Chen

Abstract: SDN (Software defined networking) provides effective technical methods for optimal resource management. However, there are resource conflicts frequent and serious in current related schemes because they mix elephant and mice flows on shared transmission paths. So, controllers in SDN have to be smart enough to detect elephant flows with low cost and then reroute elephant and mice flows in a feature-aware way. However, existing elephant flow detection schemes suffer from high bandwidth consumption and long detection time; and little literature considers mice-flow scheduling. In this paper, we propose an Efficient Sampling and Classification Approach (ESCA). Our ESCA significantly reduces sampling overhead through estimating the arrival interval of elephant flows and filtering out redundant samples, and efficiently classifies samples with a new supervised classification algorithm based on correlations among data flows. Then, based on our low-cost ESCA, we propose a novel load-balanced routing approach LBRouting that sets up paths for elephant and mice flows with different mechanisms. The theoretical analysis proofs our ESCA outperforms related schemes. Extensive experiment results further demonstrate that our ESCA can provide accurate detection with less sampled packets and shorter detection time; and our routing approach LBRouting significantly outperforms related proposals.