Multi-Layer-Mesh: A Novel Topology and SDN-Based Path Switching for Big Data Cluster Networks

Leandro Batista de Almeida, D. Magoni, Philip Perry, E. Almeida, John Murphy, Anthony Ventresque
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引用次数: 3

Abstract

Big Data technologies and tools have being used for the past decade to solve several scientific and industry problems, with Hadoop/YARN becoming the “de facto” standard for these applications, although other technologies run on top of it. As any other distributed application, those big data technologies rely heavily on the network infrastructure to read and move data from hundreds or thousands of cluster nodes. Although these technologies are based on reliable and efficient distributed algorithms, there are scenarios and conditions that can generate bottlenecks and inefficiencies, i.e., when a high number of concurrent users creates data access contention. In this paper, we propose a novel network topology called Multi-Layer-Mesh and a path switching algorithm based on SDN, that can increase the performance of a big data cluster while reducing the amount of utilized resources (network equipment), in turn reducing the energy and cooling consumption. A thorough simulation-based evaluation of our algorithms shows an average improvement in performance of 31.77% and an average decrease in resource utilization of 36.03% compared to a traditional Spine-Leaf topology, in the selected test scenarios.
多层网格:一种新的拓扑结构和基于sdn的大数据集群网络路径交换
在过去的十年里,大数据技术和工具已经被用来解决一些科学和工业问题,Hadoop/YARN成为这些应用程序的“事实上的”标准,尽管其他技术运行在它之上。与任何其他分布式应用程序一样,这些大数据技术在很大程度上依赖于网络基础设施来读取和移动来自数百或数千个集群节点的数据。尽管这些技术基于可靠和高效的分布式算法,但仍存在可能产生瓶颈和低效率的场景和条件,例如,当大量并发用户造成数据访问争用时。在本文中,我们提出了一种名为Multi-Layer-Mesh的新型网络拓扑和基于SDN的路径交换算法,可以在提高大数据集群性能的同时减少资源(网络设备)的利用率,从而降低能源和冷却消耗。在选定的测试场景中,基于仿真的算法评估显示,与传统的Spine-Leaf拓扑相比,我们的算法的性能平均提高了31.77%,资源利用率平均降低了36.03%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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