Communication in data centers often involves many parallel flows that all share the same performance goal (e.g. to minimize the average completion time). A useful abstraction, coflow, is proposed to express the communication requirements of prevalent data parallel paradigms such as MapReduce and Spark. The multiple coflow routing and scheduling problem makes it challenging to derive a good theoretical performance ratio, as coexisting coflows may compete for the same network resources such as link bandwidths. In this paper, we focus on the coflow problem in one popular data center infrastructure: the Leaf-Spine topology. We first formulate the problem and study the path selection issue on this two-tier structure. In order to minimize the average coflow completion time (CCT), we propose the Multi-hop Coflow Routing and Scheduling strategy (MCRS) and prove that our method has a reasonably good competitive ratio. Extensive experiments and large-scale simulations show that MCRS outperforms the state-of-the-art heuristic schemes under the Leaf-Spine topology.

数据中心的通信通常涉及许多并行流，这些流都共享相同的性能目标（例如，最大程度地减少平均完成时间）。有用的抽象，同流为了表达流行的数据并行范例（如MapReduce和Spark）的通信要求，建议使用。多个同流路由和调度问题使获得良好的理论性能比具有挑战性，因为共存的同流可能会争夺相同的网络资源（例如链路带宽）。在本文中，我们集中于一种流行的数据中心基础架构中的同流问题：Leaf-Spine拓扑。我们首先提出问题，并在此两层结构上研究路径选择问题。为了最小化平均同流完成时间（CCT），我们提出了多跳同流路由和调度策略（MCRS），并证明了我们的方法具有相当好的竞争比。