YARNsim:模拟Hadoop YARN

Ning Liu, Xi Yang, Xian-He Sun, John Jenkins, R. Ross
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引用次数: 18

摘要

尽管Apache Hadoop系统很受欢迎,但它的成功受到了一些问题的限制,比如单点故障、集中式作业/任务管理,以及缺乏对MapReduce以外的编程模型的支持。下一代Hadoop, Apache Hadoop YARN,旨在解决这些问题。本文提出了基于Hadoop YARN的仿真系统YARNsim。YARNsim基于并行离散事件仿真,并在模拟YARN的关键组件时提供协议级精度。YARNsim提供了一个虚拟平台,系统架构师可以在上面评估Hadoop YARN系统的设计和实现。此外,应用程序开发人员可以调整作业性能并了解不同配置之间的权衡,Hadoop YARN系统供应商可以在有限的预算下评估系统效率。为了验证YARNsim的有效性,我们使用它对两个实际系统进行了建模,并将YARNsim的实验结果与实际系统进行了比较。实验包括标准Hadoop基准测试、合成工作负载和一个生物信息学应用程序。结果表明,大多数测试用例的错误率在10%以内。实验证明,与在真实系统上进行测试和评估相比,YARNsim能够以最小的成本及时为系统设计者提供what-if分析。
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YARNsim: Simulating Hadoop YARN
Despite the popularity of the Apache Hadoop system, its success has been limited by issues such as single points of failure, centralized job/task management, and lack of support for programming models other than MapReduce. The next generation of Hadoop, Apache Hadoop YARN, is designed to address these issues. In this paper, we propose YARNsim, a simulation system for Hadoop YARN. YARNsim is based on parallel discrete event simulation and provides protocol-level accuracy in simulating key components of YARN. YARNsim provides a virtual platform on which system architects can evaluate the design and implementation of Hadoop YARN systems. Also, application developers can tune job performance and understand the tradeoffs between different configurations, and Hadoop YARN system vendors can evaluate system efficiency under limited budgets. To demonstrate the validity of YARNsim, we use it to model two real systems and compare the experimental results from YARNsim and the real systems. The experiments include standard Hadoop benchmarks, synthetic workloads, and a bioinformatics application. The results show that the error rate is within 10% for the majority of test cases. The experiments prove that YARNsim can provide what-if analysis for system designers in a timely manner and at minimal cost compared with testing and evaluating on a real system.
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