ATConf: auto-tuning high dimensional configuration parameters for big data processing frameworks
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Software
Link
https://link.springer.com/content/pdf/10.1007/s10586-022-03767-0.pdf
Reference38 articles.
1. Herodotou, H., Chen, Y., Jiaheng, L.: A survey on automatic parameter tuning for big data processing systems. ACM Comput. Surv. (CSUR) 53(2), 1–37 (2020)
2. Zhu, Y., Liu, J., Guo, M., Bao, Y., Ma, W., Liu, Z., Song, K., Yang, Y.: Bestconfig: tapping the performance potential of systems via automatic configuration tuning. In: Proceedings of the 2017 symposium on cloud computing, pp. 338–350 (2017)
3. Van Aken, D., Pavlo, A., Gordon, G.J., Zhang, B.: Automatic database management system tuning through large-scale machine learning. In: Proceedings of the 2017 ACM international conference on management of data, pp 1009–1024 (2017)
4. Yu, Z., Bei, Z., Qian, X.: Datasize-aware high dimensional configurations auto-tuning of in-memory cluster computing. In: Proceedings of the twenty-third international conference on architectural support for programming languages and operating systems, pp. 564–577 (2018)
5. Zhang, J., Liu, Y., Zhou, K., Li, G., Xiao, Z., Cheng, B., Xing, J., Wang, Y., Cheng, T., et al.: An end-to-end automatic cloud database tuning system using deep reinforcement learning. In: Proceedings of the 2019 international conference on management of data, pp. 415–432 (2019)
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