STEAMEngine: Driving MapReduce provisioning in the cloud

Michael Cardosa, Piyush Narang, A. Chandra, Himabindu Pucha, Aameek Singh
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引用次数: 31

Abstract

MapReduce has gained in popularity as a distributed data analysis paradigm, particularly in the cloud, where MapReduce jobs are run on virtual clusters. The provisioning of MapReduce jobs in the cloud is an important problem for optimizing several user as well as provider-side metrics, such as runtime, cost, throughput, energy, and load. In this paper, we present an intelligent provisioning framework called STEAMEngine that consists of provisioning algorithms to optimize these metrics through a set of common building blocks. These building blocks enable spatio-temporal tradeoffs unique to MapReduce provisioning: along with their resource requirements (spatial component), a MapReduce job runtime (temporal component) is a critical element for any provisioning algorithm. We also describe tw o novel provisioning algorithms — a user-driven performance optimization and a provider-driven energy optimization — that leverage these building blocks. Our experimental results based on an Amazon EC2 cluster and a local Xen/Hadoop cluster show the benefits of STEAMEngine through improvements in performance and energy via the use of these algorithms and building blocks.
STEAMEngine:驱动云中的MapReduce配置
MapReduce作为一种分布式数据分析范例已经获得了广泛的应用,特别是在云计算中,MapReduce作业是在虚拟集群上运行的。在云中提供MapReduce作业是一个重要的问题,它可以优化多个用户端和提供商端的指标,比如运行时、成本、吞吐量、能源和负载。在本文中,我们提出了一个名为STEAMEngine的智能配置框架,该框架由配置算法组成,通过一组通用构建块来优化这些指标。这些构建块实现了MapReduce配置特有的时空权衡:与其资源需求(空间组件)一起,MapReduce作业运行时(时间组件)是任何配置算法的关键元素。我们还描述了利用这些构建块的两种新的供应算法——用户驱动的性能优化和提供者驱动的能源优化。我们基于Amazon EC2集群和本地Xen/Hadoop集群的实验结果表明,通过使用这些算法和构建块,STEAMEngine在性能和能源方面的改进带来了好处。
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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