LADRA: Log-based abnormal task detection and root-cause analysis in big data processing with Spark
LADRA: Log-based abnormal task detection and root-cause analysis in big data processing with Spark
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LADRA：Spark 大数据处理中基于日志的异常任务检测和根本原因分析
DOI：
10.1016/j.future.2018.12.002
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Wang, Liqiang
中科院分区：
文献类型：
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作者：
Lu, Siyang;Wei, Yang;Rao, Bingbing;Tak, Byungchul;Wang, Long;Wang, Liqiang
随着大数据处理被许多领域广泛采用，大量生成的数据变得更加依赖于并行计算平台进行分析，其中Spark是使用最广泛的框架之一。Spark的异常任务可能会导致显著的性能下降，检测和诊断根本原因极具挑战性。为此，我们提出了一个创新的工具，命名LASTOS，为日志为基础的异常tasks检测和ot-causeanalysis使用Spark日志。在LANDOS中，日志解析器首先将原始日志文件转换为结构化数据并提取特征。然后，提出了一种检测方法来检测异常任务发生的位置和时间。为了分析根本原因，我们进一步提取基于这些特征的预定义因素。最后，我们利用广义回归神经网络（GRNN）来识别异常任务的根本原因。根据GRNN的加权因子，将报告的根本原因的可能性呈现给用户。LASTOS是一个离线工具，可以准确地分析异常，而无需额外的监控开销。四个潜在的根本原因，即，CPU、内存、网络和磁盘I/O。我们已经通过注入上述根本原因在三个Spark基准测试上测试了LASTON。实验结果表明，我们提出的方法是更准确的根本原因分析比其他现有的方法。
As big data processing is being widely adopted by many domains, massive amount of generated data become more reliant on the parallel computing platforms for analysis, wherein Spark is one of the most widely used frameworks. Spark’s abnormal tasks may cause significant performance degradation, and it is extremely challenging to detect and diagnose the root causes. To that end, we propose an innovative tool, namedLADRA, forlog-basedabnormal tasksdetection androot-causeanalysis using Spark logs. In LADRA, a log parser first converts raw log files into structured data and extracts features. Then, a detection method is proposed to detect where and when abnormal tasks happen. In order to analyze root causes we further extract pre-defined factors based on these features. Finally, we leverage General Regression Neural Network (GRNN) to identify root causes for abnormal tasks. The likelihood of reported root causes are presented to users according to the weighted factors by GRNN. LADRA is an off-line tool that can accurately analyze abnormality without extra monitoring overhead. Four potential root causes,i.e., CPU, memory, network, and disk I/O, are considered. We have tested LADRA atop of three Spark benchmarks by injecting aforementioned root causes. Experimental results show that our proposed approach is more accurate in the root cause analysis than other existing methods.