Abstract
Tasks are the basic unit of Spark application scheduling, and its execution is affected by various configurations of Spark cluster. Therefore, the prediction of task execution time is a challenging job. In this paper, we analyze the features of task execution procedure on different stages, and propose the method of prediction of each sub-stage execution time. Moreover, the correlative time overheads of GC and shuffle spill are analyzed in detail. As a result, we propose SPM, a task-level execution time prediction model. SPM can be used to predict the task execution time of each stage according to the input data size and configuration of parallelism. We further apply SPM to the Spark network emulation tool SNemu, which can determine the start time of each shuffle procedure for emulation effectively. Experimental results show that the prediction method can achieve high accuracy in a variety of Spark benchmarks on Hibench.
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References
Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. HotCloud 10(10–10), 95 (2010)
Hadoop Homepage. http://Hadoop.apache.org/. Accessed 4 Sept 2019
Zaharia, M., et al.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation, p. 2. USENIX Association (2012)
Gu, J., Li, Y., Tang, H., Wu, Z.: Auto-tuning spark configurations based on neural network. In: 2018 IEEE International Conference on Communications (ICC), pp. 1–6. IEEE (2018)
Popescu, A.D., Balmin, A., Ercegovac, V., Ailamaki, A.: Predict: towards predicting the runtime of large scale iterative analytics. Proc. VLDB Endow. 6(14), 1678–1689 (2013)
Nguyen, N., Khan, M.M.H., Albayram, Y., Wang, K.: Understanding the influence of configuration settings: an execution model-driven framework for apache spark platform. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 802–807. IEEE (2017)
Bhimani, J., Mi, N., Leeser, M., Yang, Z.: FIM: performance prediction for parallel computation in iterative data processing applications. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 359–366. IEEE (2017)
Venkataraman, S., Yang, Z., Franklin, M., Recht, B., Stoica, I.: Ernest: efficient performance prediction for large-scale advanced analytics. In: 13th USENIX Symposium on Networked Systems Design and Implementation (NSDI 2016), pp. 363–378 (2016)
Wang, K., Khan, M.M.H.: Performance prediction for apache spark platform. In: 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems, pp. 166–173. IEEE (2015)
Huang, S., Huang, J., Dai, J., Xie, T., Huang, B.: The hibench benchmark suite: characterization of the mapreduce-based data analysis. In: 2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010), pp. 41–51. IEEE (2010)
SNemu. https://github.com/lab821/SNemu. Accessed 4 Sept 2019
Acknowledgement
This work is supported by the National Key Research and Development Program of China (Grant No. 2016YFB1000304) and National Natural Science Foundation of China (Grant No. 1636208).
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Li, W., Hu, S., Wang, D., Chen, T., Li, Y. (2020). SPM: Modeling Spark Task Execution Time from the Sub-stage Perspective. In: Wen, S., Zomaya, A., Yang, L.T. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2019. Lecture Notes in Computer Science(), vol 11945. Springer, Cham. https://doi.org/10.1007/978-3-030-38961-1_1
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DOI: https://doi.org/10.1007/978-3-030-38961-1_1
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