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Published in:
2019 | OriginalPaper | Chapter
ASTracer: An Efficient Tracing Tool for HDFS with Adaptive Sampling
Authors : Yang Song, Yunchun Li, Shuhan Wu, Hailong Yang, Wei Li
Published in: Network and Parallel Computing
Publisher: Springer International Publishing
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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.