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

机译:多层网格:针对大数据集群网络的新型拓扑和基于SDN的路径切换

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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.
机译:在过去的十年中,大数据技术和工具已用于解决一些科学和行业问题,尽管Hadoop / YARN成为这些应用程序的“事实上”标准,但其他技术仍在此之上运行。与任何其他分布式应用程序一样,那些大数据技术严重依赖于网络基础结构来从数百或数千个群集节点读取和移动数据。尽管这些技术基于可靠且高效的分布式算法,但是在某些情况和条件下会产生瓶颈和效率低下,即,当大量并发用户创建数据访问竞争时。在本文中,我们提出了一种新颖的网络拓扑结构,称为多层网格和基于SDN的路径切换算法,可以提高大数据集群的性能,同时减少资源(网络设备)的使用量,从而减少能源和冷却消耗。在选定的测试场景中,与传统的Spine-Leaf拓扑相比,对我们算法的基于仿真的全面评估显示,与传统的Spine-Leaf拓扑相比,性能平均提高31.77%,资源利用率平均降低36.03%。

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