Performance issue monitoring, identification and diagnosis of SaaS software: a survey

Abstract

SaaS (Software-as-a-Service) is a service model provided by cloud computing. It has a high requirement for QoS (Quality of Software) due to its method of providing software service. However, manual identification and diagnosis for performance issues is typically expensive and laborious because of the complexity of the application software and the dynamic nature of the deployment environment. Recently, substantial research efforts have been devoted to automatically identifying and diagnosing performance issues of SaaS software. In this survey, we comprehensively review the different methods about automatically identifying and diagnosing performance issues of SaaS software. We divide them into three steps according to their function: performance log generation, performance issue identification and performance issue diagnosis. We then comprehensively review these methods by their development history. Meanwhile, we give our proposed solution for each step. Finally, the effectiveness of our proposed methods is shown by experiments.

Graphical abstract

Keywords

SaaS software / performance log generation / performance issue identification / performance issue diagnosis

Rui Wang is currently a lecturer with the College of Energy and Mining Engineering, Shandong University of Science and Technology, China. She received the PhD degree from Wuhan University, China in 2019. Her research interests include software engineering and data mining

Xiangbo Tian received the BE degree from the Shandong University of Science and Technology, China in 2018. He is currently working toward the PhD degree with the School of Computer Science, Wuhan University, China. His current research interests include service computing and microservices

Shi Ying is currently a professor in the School of Computer Science, Wuhan University, China. His main research interests include software engineering and artificial intelligence

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Acknowledgments

The work was supported by the National Key R&D Program of China (2022YFB3304300), the Humanities and Social Sciences Youth Foundation, Ministry of Education (23YJCZH221) and the Natural Science Foundation of Shandong Province (ZR2023QE030).

Competing interests

The authors declare that they have no competing interests or financial conflicts to disclose.

Data availability statement

The data that support the findings of this study are available from the National Disaster Reduction Center of China but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the National Disaster Reduction Center of China.

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