@Article{cmc.2020.05247,
AUTHOR = {Kun Zhu, Shi Ying, Nana Zhang, Rui Wang, Yutong Wu, Gongjin Lan, Xu Wang},
TITLE = {A Performance Fault Diagnosis Method for SaaS Software Based on GBDT Algorithm},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {62},
YEAR = {2020},
NUMBER = {3},
PAGES = {1161--1185},
URL = {http://www.techscience.com/cmc/v62n3/38347},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.05247}
}