Flow-Aware Congestion Control to Improve Throughput under TCP Incast in Datacenter Networks

Abstract

Data enter networks (DCNs) host cloud computing and various applications. In this environment, there are mice and elephant flows and the partition/aggregate communication pattern is common, so TCP in cast may often occur. Massive mice flows directly affect the elephant flows under in cast, and the standard TCP mechanism or SDN approach with centralized controller is insufficient to solve this problem. In this paper, we focused on how a flow-aware congestion control method that takes into account flow characteristics can improve throughput under in cast. It is required to establish our long-term goal that provides different network level services based on flow characteristics. We propose a switch-based method that identifies target flows and provides different congestion control using explicit congestion notification (ECN). Although our method uses ECN, it does not require any change to TCP stacks on end nodes or a dedicated queue for the targets. In our experiments, we focused on the throughput of flows, and selected elephant flows as targets because their throughput is more sensitive than mice flows. We compared the experimental results from our method to those of standard TCP, ECN, and a dedicated queue for elephants. Our method improved the aggregate throughput of elephant flows by 1.7× to 5.1× and the good put of elephant flows by 1.9× to 5.4× compared to those of the above methods under our in cast scenario.