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

被引：1

作者：

Lui, Nyalia ^{[1
]}

Al Hasan, Mohammad ^{[1
]}

Hill, James H. ^{[1
]}

机构：

[1] Indianapolis Univ, Purdue Univ Indianapolis, Indianapolis, IN 46227 USA

来源：

2021 IEEE 24TH INTERNATIONAL SYMPOSIUM ON REAL-TIME DISTRIBUTED COMPUTING (ISORC 2021) | 2021年

关键词：

Binary Classifier; More is Less; Performance Anti-pattern; Support Vector Machine;

D O I：

10.1109/ISORC52013.2021.00020

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：71 / 78

页数：8

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