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Big data deployment in containerized infrastructures through the interconnection of network namespaces

机译:通过网络命名空间的互连,在集装箱基础架构中进行大数据部署

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摘要

Big Data applications tackle the challenge of fast handling of large streams of data. Their performance is not only dependent on the data frameworks implementation and the underlying hardware but also on the deployment scheme and its potential for fast scaling. Consequently, several efforts have focused on the ease of deployment of Big Data applications, notably through the use of containerization. This technology was indeed raised to bring multitenancy and multiprocessing out of clusters, providing high deployment flexibility through lightweight container images. Recent studies have focused mostly on Docker containers. Notwithstanding, this article is actually interested in recent Singularity containers as they provide more security and support high-performance computing (HPC) environments and, in this way, they can make Big Data applications benefit from the specialized hardware of HPC. Singularity 2.x, however, does not isolate network resources as required by most Big Data components. Singularity 3.x allows allocating each container with isolated network resources, but their interconnection requires a nontrivial amount of configuration effort. In this context, this article makes a functional contribution in the form of a deployment scheme based on the interconnection of network namespaces, through underlay and overlay networking approaches, to make Big Data applications easily deployable inside Singularity containers. We provide detailed account of our deployment scheme when using both interconnection approaches in the form of a "how-to-do-it" report, and we evaluate it by comparing three Big Data applications based on Hadoop when performing on a bare-metal infrastructure and on scenarios involving Singularity and Docker instances.
机译:大数据应用解决了大量数据流快速处理的挑战。它们的性能不仅依赖于数据框架实现和底层硬件,还依赖于部署方案及其快速缩放的可能性。因此,几项努力集中在易于部署大数据应用,特别是通过使用集装箱化。这项技术确实提升以带来多租期性和多处理,通过轻量级集装箱图像提供高部署灵活性。最近的研究主要集中在码头容器上。尽管如此,本文实际上对最近的奇点容器非常感兴趣,因为它们提供了更多的安全性并支持高性能计算(HPC)环境,并以这种方式使大数据应用能够受益于HPC的专业硬件。但是,奇点2.x不会根据大多数大数据组件的要求隔离网络资源。奇点3.x允许使用隔离网络资源分配每个容器,但它们的互连需要非动力的配置工作。在此上下文中,本文以网络命名空间的互连,通过底层和覆盖网络方法来实现部署方案形式的功能贡献,以便在奇点容器内轻松部署大数据应用。我们在使用“HOW-DO-IT”报告的形式中使用两个互连方法时,我们提供了我们的部署方案的详细帐户,并且我们通过在裸机基础设施上执行基于Hadoop的基于Hadoop的三大数据应用来评估它以及涉及奇点和码头实例的情景。

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  • 来源
    《Software, practice & experience》 |2020年第7期|1087-1113|共27页
  • 作者单位

    Spienter Univ Politecn Catalunya UPC Comp Architecture Dept Spienter Barcelona Spain;

    Lenovo Lenovo Data Ctr Grp Morrisville NC USA;

    Spienter Univ Politecn Catalunya UPC Comp Architecture Dept Spienter Barcelona Spain|Spienter Barcelona Supercomp Ctr BSC Dept Comp Sci Spienter Barcelona Spain;

    Lenovo Lenovo Data Ctr Grp Morrisville NC USA;

    Lenovo Lenovo Data Ctr Grp Morrisville NC USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Big Data; Hadoop; namespaces; Singularity containers;

    机译:大数据;Hadoop;命名空间;奇点容器;
  • 入库时间 2022-08-18 21:29:00

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