Abstract
Performance self tuning in database systems is a challenge work since it is hard to identify tuning parameters and make a balance to choose proper configuration values for them. In this paper, we propose a neural network based algorithm for performance self-tuning. We first extract Automatic Workload Repository report automatically, and then identify key system performance parameters and performance indicators. We then use the collected data to construct a Neural Network model. Finally, we develop a self-tuning algorithm to tune these parameters. Experimental results for oracle database system in TPC-C workload environment show that the proposed method can dynamically improve the performance.
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Zheng, C., Ding, Z., Hu, J. (2014). Self-tuning Performance of Database Systems with Neural Network. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theory. ICIC 2014. Lecture Notes in Computer Science, vol 8588. Springer, Cham. https://doi.org/10.1007/978-3-319-09333-8_1
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DOI: https://doi.org/10.1007/978-3-319-09333-8_1
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