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Monitoring data for anomaly detection in cloud-based systems : a systematic mapping study

Hrusto, Adha LU orcid ; Ali, Nauman Bin ; Engström, Emelie LU orcid and Wang, Yuqing (2025) In ACM Transactions on Software Engineering and Methodology
Abstract
Context: Anomaly detection is crucial for maintaining cloud-based software systems, as it enables early identification and resolution of unexpected failures. Given rapid and significant advances in the anomaly detection domain and the complexity of its industrial implementation, an overview of techniques that utilize real-world operational data is needed.

Aim: This study aims to complement existing research with an extensive catalog of the techniques and monitoring data used for detecting anomalies affecting the performance or reliability of cloud-based software systems that have been developed and/or evaluated in a real-world context.

Method: We perform a systematic mapping study to examine the literature on anomaly... (More)
Context: Anomaly detection is crucial for maintaining cloud-based software systems, as it enables early identification and resolution of unexpected failures. Given rapid and significant advances in the anomaly detection domain and the complexity of its industrial implementation, an overview of techniques that utilize real-world operational data is needed.

Aim: This study aims to complement existing research with an extensive catalog of the techniques and monitoring data used for detecting anomalies affecting the performance or reliability of cloud-based software systems that have been developed and/or evaluated in a real-world context.

Method: We perform a systematic mapping study to examine the literature on anomaly detection in cloud-based systems, particularly focusing on the usage of real-world monitoring data, with the aim of identifying key data categories, tools, data preprocessing, and anomaly detection techniques.

Results: Based on a review of 104 papers, we categorize monitoring data by structure, types, and origins and the tools used for data collection and processing. We offer a comprehensive overview of data preprocessing and anomaly detection techniques mapped to different data categories. Our findings highlight practical challenges and considerations in applying these techniques in real-world cloud environments.

Conclusion: The findings help practitioners and researchers identify relevant data categories and select appropriate data preprocessing and anomaly detection techniques for their specific operational environments, which is important for improving the reliability and performance of cloud-based systems. (Less)
Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Contribution to journal
publication status
in press
subject
in
ACM Transactions on Software Engineering and Methodology
pages
37 pages
publisher
Association for Computing Machinery (ACM)
ISSN
1049-331X
DOI
10.1145/3744556
language
English
LU publication?
yes
id
ce3fa0b1-6041-430d-b276-9cc47f5e3db9
date added to LUP
2025-10-27 15:04:53
date last changed
2025-10-30 15:17:20
@article{ce3fa0b1-6041-430d-b276-9cc47f5e3db9,
  abstract     = {{Context: Anomaly detection is crucial for maintaining cloud-based software systems, as it enables early identification and resolution of unexpected failures. Given rapid and significant advances in the anomaly detection domain and the complexity of its industrial implementation, an overview of techniques that utilize real-world operational data is needed. <br/><br/>Aim: This study aims to complement existing research with an extensive catalog of the techniques and monitoring data used for detecting anomalies affecting the performance or reliability of cloud-based software systems that have been developed and/or evaluated in a real-world context.<br/><br/>Method: We perform a systematic mapping study to examine the literature on anomaly detection in cloud-based systems, particularly focusing on the usage of real-world monitoring data, with the aim of identifying key data categories, tools, data preprocessing, and anomaly detection techniques.<br/><br/>Results: Based on a review of 104 papers, we categorize monitoring data by structure, types, and origins and the tools used for data collection and processing. We offer a comprehensive overview of data preprocessing and anomaly detection techniques mapped to different data categories. Our findings highlight practical challenges and considerations in applying these techniques in real-world cloud environments.<br/><br/>Conclusion: The findings help practitioners and researchers identify relevant data categories and select appropriate data preprocessing and anomaly detection techniques for their specific operational environments, which is important for improving the reliability and performance of cloud-based systems.}},
  author       = {{Hrusto, Adha and Ali, Nauman Bin and Engström, Emelie and Wang, Yuqing}},
  issn         = {{1049-331X}},
  language     = {{eng}},
  month        = {{06}},
  publisher    = {{Association for Computing Machinery (ACM)}},
  series       = {{ACM Transactions on Software Engineering and Methodology}},
  title        = {{Monitoring data for anomaly detection in cloud-based systems : a systematic mapping study}},
  url          = {{http://dx.doi.org/10.1145/3744556}},
  doi          = {{10.1145/3744556}},
  year         = {{2025}},
}