Achieving Fast Convergence and High Efficiency using Differential Explicit Feedback in Data Center

Abstract

Since most flows are short-lived in data center networks, fast convergence becomes very important to help the short flows effectively utilize high bandwidth. Though current feedback-based transport control protocols (TCPs) provide fast convergence via fine-grained explicit congestion information from customized switches, they unavoidably incur large traffic overhead for widely existing small packets in data center applications, resulting in suboptimal network efficiency. To solve this issue, we propose a datacenter TCP based on differential feedbacks, called DECN, to achieve fast convergence without any traffic overhead. Specifically, DECN feeds rate difference between the target and current rate back to the source by using multiple consecutive packets. The experimental results of NS2 simulation and testbed implementation show that DECN achieves comparable fast convergence as XCP without incurring any extra feedback overhead. Compared with the state-of-the-art feedback-based TCPs, DECN reduces the flow completion time by up to 34.1% in typical data center applications.