• Journal of Internet Computing and Services
    ISSN 2287 - 1136(Online) / ISSN 1598 - 0170 (Print)
    http://jics.or.kr/

A machine learning framework for performance anomaly detection


Muhammad Hasnain, Muhammad Fermi Pasha, Imran Ghani, Seung Ryul Jeong, Aitizaz Ali, Journal of Internet Computing and Services, Vol. 23, No. 2, pp. 97-105, Apr. 2022
10.7472/jksii.2022.23.2.97, Full Text:
Keywords: Web services update, undetected regression anomalies, Performance metrics, services integrate

Abstract

Web services show a rapid evolution and integration to meet the increased users' requirements. Thus, web services undergo updates and may have performance degradation due to undetected faults in the updated versions. Due to these faults, many performances and regression anomalies in web services may occur in real-world scenarios. This paper proposed applying the deep learning model and innovative explainable framework to detect performance and regression anomalies in web services. This study indicated that upper bound and lower bound values in performance metrics provide us with the simple means to detect the performance and regression anomalies in updated versions of web services. The explainable deep learning method enabled us to decide the precise use of deep learning to detect performance and anomalies in web services. The evaluation results of the proposed approach showed us the detection of unusual behavior of web service. The proposed approach is efficient and straightforward in detecting regression anomalies in web services compared with the existing approaches.


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Cite this article
[APA Style]
Muhammad Hasnain, Muhammad Fermi Pasha, Imran Ghani, Seung Ryul Jeong, & Aitizaz Ali (2022). A machine learning framework for performance anomaly detection. Journal of Internet Computing and Services, 23(2), 97-105. DOI: 10.7472/jksii.2022.23.2.97.

[IEEE Style]
M. Hasnain, M. F. Pasha, I. Ghani, S. R. Jeong and A. Ali, "A machine learning framework for performance anomaly detection," Journal of Internet Computing and Services, vol. 23, no. 2, pp. 97-105, 2022. DOI: 10.7472/jksii.2022.23.2.97.

[ACM Style]
Muhammad Hasnain, Muhammad Fermi Pasha, Imran Ghani, Seung Ryul Jeong, and Aitizaz Ali. 2022. A machine learning framework for performance anomaly detection. Journal of Internet Computing and Services, 23, 2, (2022), 97-105. DOI: 10.7472/jksii.2022.23.2.97.