Adaptive Path Isolation for Elephant and Mice Flows by Exploiting Path Diversity in Datacenters

Abstract

Resource competition and conflicts in datacenter networks (DCNs) are frequent and intense. They become inevitable when mixing elephant and mice flows on shared transmission paths, resulting in arbitration between throughput and latency and performance degradation. We propose a novel flow scheduling scheme, Freeway, that leverages on path diversity in the DCN topology to guarantee, simultaneously, mice flow completion within deadline and high network utilization. Freeway adaptively partitions the available paths into low latency and high throughput paths and provides different transmission services for each category. A M/G/1-based model is developed to theoretically obtain the highest value of average delay over the path that will guarantee for 99% of mice flows their completion time before the deadline. Based on this bound, Freeway proposes a dynamic path partitioning algorithm to adjust dynamically with varying traffic load the number of low latency and high throughput paths. While mice flows are transmitted over low latency paths using a simple equal cost multiple path (ECMP) scheduling, Freeway load balances elephant flows on different high-throughput paths. We evaluate Freeway in a series of simulation on a large scale topology and use real traces. Our evaluation results show that Freeway significantly reduces the mice flows completion time within deadlines, while achieving remarkable throughput compared with current schemes. It is remarkable that Freeway does not need any change of DCN switch fabrics or scheduling algorithms and can be deployed easily on any generic datacenter network with switches implementing VLANs and trunking.