Abstract
Many data-driven methods and machine learning techniques are constantly being applied to the database management system (DBMS), which are based on the judgment of future workloads to achieve a better tuning result. We propose a novel multi-step workload forecasting approach named TEALED which applies time-sensitive empirical mode decomposition and auto long short-term memory based encoder-decoder to predict resource utilization and query arrival rates for DBMSs. We first improve the empirical mode decomposition method by considering time translation and extending short series. Then we utilize the encoder-decoder network to extract features from decomposed workloads and generate workload predictions. Moreover, we combine hyper-parameter search technologies to guarantee performance under varying workloads. The experiment results show the effectiveness and robustness of TEALED, and indicate the ability of multi-step workload forecasting.
This work was supported by the National Key R&D Program of China [2020YFB1707903]; the National Natural Science Foundation of China [61872238, 61972254], Shanghai Municipal Science and Technology Major Project [2021SHZDZX0102], and the ByteDance Research Project [CT20211123001686].
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Alibaba. Alibaba cluster trace. In: Alibaba Cluster Traces v2018. https://github.com/alibaba/clusterdata (2018)
Bergstra, J.S., Bardenet, R., Bengio, Y., Kégl, B.: Algorithms for hyper-parameter optimization. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 2546–2554 (2011)
Holze, M., Ritter, N.: Towards workload shift detection and prediction for autonomic databases. In: Proceedings of the ACM First Ph.D. Workshop in CIKM, pp. 109–116 (2007)
Huang, N.E., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Roy. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 454, 903–995 (1998)
Li, G., Zhou, X., Li, S., Gao, B.: Qtune: a query-aware database tuning system with deep reinforcement learning. In: Proceedings of the VLDB Endowment (PVLDB), vol. 12, pp. 2118–2130 (2019)
Liu, C., Mao, W., Gao, Y., Gao, X., Li, S., Chen, G.: Adaptive recollected RNN for workload forecasting in database-as-a-service. In: International Conference on Service-Oriented Computing (ICSOC), pp. 431–438 (2020)
Ma, L., Van Aken, D., Hefny, A., Mezerhane, G., Pavlo, A., Gordon, G.J.: Query-based workload forecasting for self-driving database management systems. In: ACM International Conference on Management of Data (SIGMOD), pp. 631–645 (2018)
Mozafari, B., Curino, C., Jindal, A., Madden, S.: Performance and resource modeling in highly-concurrent OLTP workloads. In: ACM International Conference on Management of Data (SIGMOD), pp. 301–312 (2013)
Ortiz, J., Balazinska, M., Gehrke, J., Keerthi, S.S.: Learning state representations for query optimization with deep reinforcement learning. In: Workshop on Data Management for End-to-End Machine Learning (DEEM) (2018)
Pavlo, A., et al.: Self-driving database management systems. In: Conference on Innovative Data Systems Research (CIDR), pp. 1–6 (2017)
Pavlo, A., Jones, E.P., Zdonik, S.: On predictive modeling for optimizing transaction execution in parallel OLTP systems. In: Proceedings of the VLDB Endowment (PVLDB), vol. 5, pp. 2150–8097 (2011)
Pham, H., Guan, M., Zoph, B., Le, Q., Dean, J.: Efficient neural architecture search via parameters sharing. In: International Conference on Machine Learning (ICML), pp. 4095–4104 (2018)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 3104–3112 (2014)
Van Aken, D., Pavlo, A., Gordon, G.J., Zhang, B.: Automatic database management system tuning through large-scale machine learning. In: ACM International Conference on Management of Data (SIGMOD), pp. 1009–1024 (2017)
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Huang, X., Cheng, Y., Gao, X., Chen, G. (2022). TEALED: A Multi-Step Workload Forecasting Approach Using Time-Sensitive EMD and Auto LSTM Encoder-Decoder. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13246. Springer, Cham. https://doi.org/10.1007/978-3-031-00126-0_55
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DOI: https://doi.org/10.1007/978-3-031-00126-0_55
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