Cormorant: Running Analytic Queries on MapReduce with Collaborative Software-Defined Networking

Pengcheng Xiong, Xin He, Hakan Hacıgümüş, P. Shenoy
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引用次数: 3

Abstract

MapReduce is a popular choice for executing analytic workloads over large datasets on clusters of commodity machines. Due to the distributed nature of such systems, network resource bottlenecks can adversely affect performance, especially when multiple applications share the network. One of the significant barriers to reducing the occurrence and impact of such bottlenecks is the current separation between MapReduce and network management and routing. Fortunately, the emergence of software-defined networking (SDN) is removing the barriers to cooperation between Hadoop and the network. To explore the opportunity this creates, we focus on how we can use the capabilities of SDN to create a more collaborative relationship between MapReduce and the network underneath. We demonstrate the effectiveness of this collaboration through the implementation of and experiments with a system we call Cormorant. Experimental results show up to 38% improvement for analytic query performance, beyond the benefits achievable by independently optimizing MapReduce schedulers and network flow schedulers.
Cormorant:使用协同软件定义网络在MapReduce上运行分析查询
MapReduce是在商用机器集群上对大型数据集执行分析工作负载的流行选择。由于此类系统的分布式特性,网络资源瓶颈可能会对性能产生不利影响,特别是当多个应用程序共享网络时。减少这种瓶颈的发生和影响的一个重要障碍是MapReduce与网络管理和路由之间的分离。幸运的是,软件定义网络(SDN)的出现正在消除Hadoop与网络之间合作的障碍。为了探索这带来的机会,我们将重点放在如何利用SDN的功能在MapReduce和底层网络之间建立一种更加协作的关系。我们通过一个我们称之为Cormorant的系统的实施和实验来证明这种合作的有效性。实验结果表明,分析查询性能提高了38%,超出了独立优化MapReduce调度器和网络流调度器所能获得的好处。
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