Real-time Detection of the More is Less Performance Anti-Pattern in MySQL Databases

This paper presents an approach for real-time detection of the More is Less software performance anti-pattern in MySQL databases. This project uses dynamic analysis paired with machine learning techniques to train a binary classifier on a collection of system-level metrics, database metrics, and MySQL status variables. After training, the classifier is loaded, at run-time, into the dynamic analysis code where the model can classify unlabeled data in real-time. The results of our approach show that the binary classifier can predict the More is Less software performance anti-pattern with 99.1% sensitivity.