Open Access
ARTICLE
A Performance Fault Diagnosis Method for SaaS Software Based on GBDT Algorithm
Kun Zhu1, Shi Ying1, *, 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.
Keywords
Cite This Article
K. Zhu, S. Ying, N. Zhang, R. Wang, Y. Wu
et al., "A performance fault diagnosis method for saas software based on gbdt algorithm,"
Computers, Materials & Continua, vol. 62, no.3, pp. 1161–1185, 2020.
Citations