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ASTracer: An Efficient Tracing Tool for HDFS with Adaptive Sampling

  • Yang Song
  • Yunchun Li
  • Shuhan Wu
  • Hailong YangEmail author
  • Wei Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11783)

Abstract

Existing distributed tracing tools such as HTrace use static probabilistic samplers to collect the function call trees for performance analysis, which may fail to capture important but less executed function call trees and thus miss the opportunities for performance optimization. To address the above problem, we propose ASTracer, a new distributed tracing tool with two adaptive samplers. The advantage of adaptive samplers is that they can adjust the sampling rate dynamically, which is able to capture comprehensive function call trees and in the meanwhile maintain the size of trace file acceptable. In addition, we propose an auto-tuning mechanism to search for the optimal parameter settings of the adaptive samplers in ASTracer. The experiment results demonstrate the adaptive samplers are more effective in tracing the function call trees compared to probabilistic sampler. Moreover, we provide several case studies to demonstrate the usage of ASTracer in identifying potential performance bottlenecks.

Keywords

HDFS Distributed tracing tool Adaptive sampling 

Notes

Acknowledgement

This work is supported by National Key Research and Development Program of China (Grant No. 2016YFB1000304) and National Natural Science Foundation of China (Grant No. 61502019). Hailong Yang is the corresponding author.

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Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Yang Song
    • 1
  • Yunchun Li
    • 1
  • Shuhan Wu
    • 1
  • Hailong Yang
    • 1
    Email author
  • Wei Li
    • 1
  1. 1.School of Computer Science and EngineeringBeihang UniversityBeijingChina

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