A performance fault diagnosis method for SaaS software based on GBDT algorithm

Kun Zhu, Shi Ying*, Nana Zhang, Rui Wang, Yutong Wu, Gongjin Lan, Xu Wang

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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.

Original languageEnglish
Pages (from-to)1161-1185
Number of pages25
JournalComputers, Materials and Continua
Volume62
Issue number3
DOIs
Publication statusPublished - 2020

Keywords

  • GBDT algorithm
  • Performance fault diagnosis
  • Performance log
  • SaaS software

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