A Frequency Scaling based Performance Indicator Framework for Big Data Systems

Chen Yang, Zhihui Du, Xiaofeng Meng, Yongjie Du, Zhiqiang Duan

It is important for big data systems to identify their performance bottleneck. However, the popular indicators such as resource utilizations, are often misleading and incomparable with each other. In this paper, a novel indicator framework which can directly compare the impact of different indicators with each other is proposed to identify and analyze the performance bottleneck efficiently. A methodology which can construct the indicator from the performance change with the CPU frequency scaling is described. Spark is used as an example of a big data system and two typical SQL benchmarks are used as the workloads to evaluate the proposed method. Experimental results show that the proposed method is accurate compared with the resource utilization method and easy to implement compared with some white-box method. Meanwhile, the analysis with our indicators lead to some interesting findings and valuable performance optimization suggestions for big data systems.

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