Abstract
Nowadays, Database-as-a-Service (DBaaS) plays a more and more important role in the era of big data due to its convenience and manageable capacity. However, with increasing complexity of data-driven applications, the management of database systems becomes intractable. To achieve the self-management of resources, forecasting the workload turns out to be essential. In this paper, we propose a novel machine learning based model, named Adaptive Recollected Recurrent Neural Network (AR-RNN) to help DBaaS managers better capture historical information and predict future workload with a recollection mechanism based multi-encoder and an attention mechanism based decoder architecture. Experiments on two real-world datasets show that our model outperforms both traditional and other machine learning methods for workload prediction.
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References
Assaf, R., Schumann, A.: Explainable deep neural networks for multivariate time series predictions. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI, pp. 6488–6490 (2019)
Calheiros, R.N., Masoumi, E., Ranjan, R., Buyya, R.: Workload prediction using ARIMA model and its impact on cloud applications’ QoS. IEEE Trans. Cloud Comput. 3(4), 449–458 (2015)
Chatigny, P., Patenaude, J., Wang, S.: Financial time series representation learning. CoRR (2020). https://arxiv.org/abs/2003.12194
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP, pp. 1724–1734 (2014)
Cinar, Y.G., Mirisaee, H., Goswami, P., Gaussier, E., Aït-Bachir, A., Strijov, V.: Position-based content attention for time series forecasting with sequence-to-sequence RNNs. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.-S.M. (eds.) ICONIP 2017. LNCS, vol. 10638, pp. 533–544. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70139-4_54
Curran, T.: Brain potentials of recollection and familiarity. Mem. Cogn. 28, 923–938 (2000)
Graves, A.: Adaptive computation time for recurrent neural networks. CoRR (2016)
Kong, W., Dong, Z.Y., Jia, Y., Hill, D.J., Xu, Y., Zhang, Y.: Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Trans. Smart Grid 10(1), 841–851 (2019)
Lehner, W., Sattler, K.: Database as a service (DBaaS). In: Proceedings of the 26th International Conference on Data Engineering ICDE, pp. 1216–1217 (2010)
Liu, C., Shang, Y., Duan, L., Chen, S., Liu, C., Chen, J.: Optimizing workload category for adaptive workload prediction in service clouds. In: Barros, A., Grigori, D., Narendra, N.C., Dam, H.K. (eds.) ICSOC 2015. LNCS, vol. 9435, pp. 87–104. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-48616-0_6
Ma, L., Aken, D.V., Hefny, A., Mezerhane, G., Pavlo, A., Gordon, G.J.: Query-based workload forecasting for self-driving database management systems. In: Proceedings of the 2018 International Conference on Management of Data, SIGMOD, pp. 631–645 (2018)
Acknowledgements
This work was supported by the National Key R&D Program of China [2019YFB2102200]; the National Natural Science Foundation of China [61872238, 61972254], the CCF-Huawei Database System Innovation Research Plan [CCF-Huawei DBIR2019002A], and the CCF-Tencent Open Research Fund [FR202001].
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Liu, C., Mao, W., Gao, Y., Gao, X., Li, S., Chen, G. (2020). Adaptive Recollected RNN for Workload Forecasting in Database-as-a-Service. In: Kafeza, E., Benatallah, B., Martinelli, F., Hacid, H., Bouguettaya, A., Motahari, H. (eds) Service-Oriented Computing. ICSOC 2020. Lecture Notes in Computer Science(), vol 12571. Springer, Cham. https://doi.org/10.1007/978-3-030-65310-1_30
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DOI: https://doi.org/10.1007/978-3-030-65310-1_30
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