论文标题
肖邦：将分布式和集中调整器组合用于自调整数据中心网络
Chopin: Combining Distributed and Centralized Schedulers for Self-Adjusting Datacenter Networks
论文作者
论文摘要
分布式和以数据为中心的应用程序的性能通常取决于互连网络。新兴的可重新配置数据中心网络（RDCN）是改善数据中心吞吐量的特别创新方法。依靠动态的光学拓扑结构可以以需求意识来调整工作量，RDCN允许在通信模式中利用时间和空间位置，并为经常通信的架子提供拓扑快捷方式。但是，主要的挑战涉及如何以可扩展的方式实现RDCN的需求意识。 本文介绍并评估了肖邦（Chopin），这是一种自我调整网络的混合调度程序，通过结合集中式和分布式方法，可以在低开销中提供需求意识。肖邦利用全球信息，通过其较慢的集中调度程序将光电电路分配给大象流。肖邦的分布式调度程序是更快的数量级，可以迅速对流量变化做出反应，并通过仅使用本地信息并分别在每个机架上运行，并相应地调整光电电路。
The performance of distributed and data-centric applications often critically depends on the interconnecting network. Emerging reconfigurable datacenter networks (RDCNs) are a particularly innovative approach to improve datacenter throughput. Relying on a dynamic optical topology which can be adjusted towards the workload in a demand-aware manner, RDCNs allow to exploit temporal and spatial locality in the communication pattern, and to provide topological shortcuts for frequently communicating racks. The key challenge, however, concerns how to realize demand-awareness in RDCNs in a scalable fashion. This paper presents and evaluates Chopin, a hybrid scheduler for self-adjusting networks that provides demand-awareness at low overhead, by combining centralized and distributed approaches. Chopin allocates optical circuits to elephant flows, through its slower centralized scheduler, utilizing global information. Chopin's distributed scheduler is orders of magnitude faster and can swiftly react to changes in the traffic and adjust the optical circuits accordingly, by using only local information and running at each rack separately.
