Performance Diagnosis of Oracle Database Systems Based on Image Encoding and VGG16 Model

This paper proposes a novel multivariate performance diagnostic approach for the Oracle database systems to detect performance degradation and crashes during database operations and maintenance. It was based on three technologies: image encoding, image concatenation, and deep convolutional network. Instantaneous variation and magnitude information of the time series were acquired by Heatmap and Recurrence Plot (RP) from databases. Moreover, the Heatmap and RP were concatenated in order to fully extract complementary information. Finally, the concatenated images of Heatmap and RP were used to train the VGG16 model for database performance diagnosis. The quantitative analysis demonstrated a good increment of accuracy in Heatmap and RP based on the same deep learning networks compared with other image encodings of Gramian Angular Difference Field (GADF), Gramian Angular Summation Fields (GASF), and Markov Transition Fields (MTF). Meanwhile, concatenated images of Heatmap and RP can improve the accuracy by 1% compared to single Heatmap input. The result of the trained model applied to multivariate database diagnosis shows an accuracy of 95.3%. Thus, our method can more effectively and accurately diagnose the performance of the Oracle database systems.