Open Access

ARTICLE

A Performance Fault Diagnosis Method for SaaS Software Based on GBDT Algorithm

Kun Zhu¹, Shi Ying^{1, *}, Nana Zhang¹, Rui Wang¹, Yutong Wu¹, Gongjin Lan², Xu Wang²

1 School of Computer Science, Wuhan University, Wuhan, 430072, China.
2 Department of Computer Science, Vrije University Amsterdam, Amster-dam, 1081HV, The Netherlands.

* Corresponding Author: Shi Ying. Email: .

Computers, Materials & Continua 2020, 62(3), 1161-1185. https://doi.org/10.32604/cmc.2020.05247

Abstract

SaaS software that provides services through cloud platform has been more widely used nowadays. However, when SaaS software is running, it will suffer from performance fault due to factors such as the software structural design or complex environments. It is a major challenge that how to diagnose software quickly and accurately when the performance fault occurs. For this challenge, we propose a novel performance fault diagnosis method for SaaS software based on GBDT (Gradient Boosting Decision Tree) algorithm. In particular, we leverage the monitoring mean to obtain the performance log and warning log when the SaaS software system runs, and establish the performance fault type set and determine performance log feature. We also perform performance fault type annotation for the performance log combined with the analysis result of the warning log. Moreover, we deal with the incomplete performance log and the type non-equalization problem by using the mean filling for the same type and combination of SMOTE (Synthetic Minority Oversampling Technique) and undersampling methods. Finally, we conduct an empirical study combined with the disaster reduction system deployed on the cloud platform, and it demonstrates that the proposed method has high efficiency and accuracy for the performance diagnosis when SaaS software system runs.

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Citations