BigRoots：一种有效的大数据系统掉队根源分析方法
BigRoots: An Effective Approach for Root-cause Analysis of Stragglers in Big Data System
摘要：
通常认为，掉队者对大数据系统的性能有很大的影响。然而，造成掉队的原因是复杂的。以往的工作主要集中在掉队者检测、调度级优化和粗粒度原因分析等方面。这些方法无法提供有价值的见解来帮助用户优化他们的程序。在本文中，我们提出了BigRoots，一个通用的方法，结合框架和系统的特点，在大数据系统中的落伍者的根本原因分析。BigRoots考虑了大数据框架的特性，如shuffle读/写字节和JVM垃圾回收时间，以及系统资源利用率，如CPU，I/O和网络，能够检测出掉队者的内部和外部根源。我们通过在不同的系统组件中注入高资源利用率来验证BigRoots，并进行案例研究来分析Hibench中的不同工作负载。实验结果表明，BigRoots算法能够有效地识别出产生掉队的根本原因，为性能优化提供指导。
英文摘要：
Stragglers are commonly believed to have a great impact on the performance of big data system. However, the reason to cause straggler is complicated. Previous works mostly focus on straggler detection, schedule level optimization and coarse-grained cause analysis. These methods cannot provide valuable insights to help users optimize their programs. In this paper, we propose BigRoots, a general method incorporating both framework and system features for root-cause analysis of stragglers in big data system. BigRoots considers features from big data framework such as shuffle read/write bytes and JVM garbage collection time, as well as system resource utilization such as CPU, I/O and network, which is able to detect both internal and external root causes of stragglers. We verify BigRoots by injecting high resource utilization across different system components and perform case studies to analyze different workloads in Hibench. The experimental results demonstrate that BigRoots is effective to identify the root cause of stragglers and provide useful guidance for performance optimization.
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