Journal of Computer Science and Technology ›› 2019, Vol. 34 ›› Issue (6): 1167-1184.doi: 10.1007/s11390-019-1968-y

Special Issue: Data Management and Data Mining

• Data Management and Data Mining •     Next Articles

HybridTune: Spatio-Temporal Performance Data Correlation for Performance Diagnosis of Big Data Systems

Rui Ren1,2, Member, CCF, IEEE, Jiechao Cheng3, Xi-Wen He1, Lei Wang1, Member, CCF, Jian-Feng Zhan1,*, Member, CCF, ACM, IEEE, Wan-Ling Gao1, Member, CCF, ACM, IEEE, Chun-Jie Luo1,2, Member, CCF   

  1. 1 Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;
    2 University of Chinese Academy of Sciences, Beijing 100049, China;
    3 School of Computing, National University of Singapore, Singapore 117417, Singapore
  • Received:2018-09-06 Revised:2019-09-04 Online:2019-11-16 Published:2019-11-16
  • Contact: Jian-Feng Zhan E-mail:zhanjianfeng@ict.ac.cn
  • About author:Rui Ren received her B.S. degree in computer science from the Sichuan University, Chengdu, in 2009, her M.S. degree in computer architecture from Chinese Academy of Sciences, Beijing, in 2012, and her Ph.D. degree in computer software and theory from Chinese Academy of Sciences, Beijing, in 2019. She is currently an engineer in the Institute of Computing Technology, Chinese Academy of Sciences, Beijing. Her research interests include big data, performance analysis and optimization.
  • Supported by:
    This work is supported by the National Key Research and Development Program of China under Grant No. 2016YFB1000601.

With tremendous growing interests in Big Data, the performance improvement of Big Data systems becomes more and more important. Among many steps, the first one is to analyze and diagnose performance bottlenecks of the Big Data systems. Currently, there are two major solutions. One is the pure data-driven diagnosis approach, which may be very time-consuming; the other is the rule-based analysis method, which usually requires prior knowledge. For Big Data applications like Spark workloads, we observe that the tasks in the same stages normally execute the same or similar codes on each data partition. On basis of the stage similarity and distributed characteristics of Big Data systems, we analyze the behaviors of the Big Data applications in terms of both system and micro-architectural metrics of each stage. Furthermore, for different performance problems, we propose a hybrid approach that combines prior rules and machine learning algorithms to detect performance anomalies, such as straggler tasks, task assignment imbalance, data skew, abnormal nodes and outlier metrics. Following this methodology, we design and implement a lightweight, extensible tool, named HybridTune, and measure the overhead and anomaly detection effectiveness of HybridTune using the BigDataBench benchmarks. Our experiments show that the overhead of HybridTune is only 5%, and the accuracy of outlier detection algorithm reaches up to 93%. Finally, we report several use cases diagnosing Spark and Hadoop workloads using BigDataBench, which demonstrates the potential use of HybridTune.

Key words: Big Data system; spatio-temporal correlation; rule-based diagnosis; machine learning;

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