Republic: Data Multicast Meets Hybrid Rack-Level Interconnections in Data Center

Xiaoye Sun, Yiting Xia, Simbarashe Dzinamarira, X. Huang, Dingming Wu, T. Ng
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引用次数: 11

Abstract

Data multicast is a crucial data transfer pattern in distributed big-data processing. However, due to the lack of network and system level support, data processing relies on unicast-based application layer multicast. In recent years, there has been a surge in interest in using various emerging circuit switching technologies to build data centers having hybrid packet-circuit switched rack-level interconnections, i.e., hybrid data centers. These physical layer innovations fundamentally change the inter-rack communication capability, especially the capability of multicast communication. We propose Republic, a complete system that addresses the challenging issues in achieving high-performance data multicast in hybrid data centers. Republic abstracts the underlying network complexity as a data multicast service and provides a unified Republic API for data center applications requesting data multicast. Republic is implemented and deployed in a hybrid data center testbed. Testbed evaluation shows that Republic can improve data multicast in Apache Spark machine learning applications by as much as 4.0x.
共和国:数据多播满足数据中心混合机架级互连
数据组播是分布式大数据处理中一种重要的数据传输方式。然而,由于缺乏网络和系统级的支持,数据处理依赖于基于单播的应用层组播。近年来,人们对使用各种新兴电路交换技术来构建具有混合分组电路交换机架级互连的数据中心(即混合数据中心)的兴趣激增。这些物理层的创新从根本上改变了机架间通信的能力,特别是组播通信的能力。我们提出Republic,这是一个完整的系统,解决了在混合数据中心中实现高性能数据多播的挑战性问题。Republic将底层网络复杂性抽象为数据多播服务,并为请求数据多播的数据中心应用程序提供统一的Republic API。Republic在混合数据中心测试平台中实现和部署。试验台评估表明,Republic可以将Apache Spark机器学习应用程序中的数据多播提高4.0倍。
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