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http://dx.doi.org/10.7472/jksii.2022.23.2.97

A machine learning framework for performance anomaly detection  

Hasnain, Muhammad (School of Information Technology, Monash University)
Pasha, Muhammad Fermi (School of Information Technology, Monash University)
Ghani, Imran (Virginia Military Institute)
Jeong, Seung Ryul (Graduate School of Business IT, Kookmin University)
Ali, Aitizaz (School of Information Technology, Monash University)
Publication Information
Journal of Internet Computing and Services / v.23, no.2, 2022 , pp. 97-105 More about this Journal
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.
Keywords
Web services update; undetected regression anomalies; performance metrics; services integrate;
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1 Ahmad, T., D. Truscan, and I. Porres, Identifying worst-case user scenarios for performance testing of web applications using Markov-chain workload models. Future Generation Computer Systems, 87, p. 910-920, 2018. http://dx.doi.org/10.1016/j.future.2018.01.042   DOI
2 S. Kardani Moghaddam, R. Buyya, and K. Ramamohanarao, "Performance anomaly detection using isolation trees in heterogeneous workloads of web applications in computing clouds," Concurrency and Computation: Practice and Experience, vol. 31, no. 20, p. e5306, 2019. http://dx.doi.org/10.1002/cpe.5306   DOI
3 M. Pradel, M. Huggler, and T. R. Gross, "Performance regression testing of concurrent classes," in Proceedings of the 2014 International Symposium on Software Testing and Analysis, pp. 13-25, 2014. https://doi.org/10.1145/2610384.2610393   DOI
4 Martin, A.G., et al., An approach to detect user behaviour anomalies within identity federations. Computers & Security, p. 102356, 2021. https://doi.org/10.1016/j.cose.2021.102356   DOI
5 ElSayed, M.S., et al., A novel hybrid model for intrusion detection systems in SDNs based on CNN and a new regularization technique. Journal of Network and Computer Applications, 191, p. 103160, 2021. https://doi.org/10.1016/j.jnca.2021.103160   DOI
6 Nedelkoski, S., J. Cardoso, and O. Kao. Anomaly detection from system tracing data using multimodal deep learning. in 2019 IEEE 12th International Conference on Cloud Computing (CLOUD), IEEE, 2019. https://doi.org/10.1109/CLOUD.2019.00038   DOI
7 Ahmed, T.M., et al. Studying the effectiveness of application performance management (apm) tools for detecting performance regressions for web applications: an experience report. in 2016 IEEE/ACM13th Working Conference on Mining Software Repositories (MSR). IEEE, 2016. https://doi.org/10.1145/2901739.2901774   DOI
8 Ocariza Jr, F.S. and B. Zhao, Localizing software performance regressions in web applications by comparing execution timelines.Software Testing, Verification and Reliability, 31(5): p. e1750, 2021. https://doi.org/10.1002/stvr.1750   DOI
9 Lin, J., P. Chen, and Z. Zheng. Microscope: Pinpoint performance issues with causal graphs in micro-service environments. in International Conference on Service-Oriented Computing, Springer, 2018. http://dx.doi.org/10.1007/978-3-030-03596-9_1   DOI
10 Huang, S. and K. Lei, IGAN-IDS: An imbalanced generative adversarial network towards intrusion detection system in ad-hoc networks. Ad Hoc Networks, 105, p. 102177, 2020. https://doi.org/10.1016/j.adhoc.2020.102177   DOI
11 Hasnain, M., et al. An efficient performance testing of web services. in 2019 22nd International Multitopic Conference (INMIC), IEEE, 2019. http://10.1109/INMIC48123.2019.9022763   DOI
12 Malik, H. and E.M. Shakshuki, Classification of post-deployment performance diagnostic techniques for large-scale software systems. Procedia Computer Science, 37, p. 244-251, 2014. http://dx.doi.org/10.1016/j.procs.2014.08.036   DOI
13 Shi, J., G. He, and X. Liu. Anomaly detection for key performance indicators through machine learning. in 2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC), IEEE, 2018. https://doi.org/10.1109/ICNIDC.2018.8525714   DOI
14 Delgado Perez, P., et al., Performance mutation testing. Software Testing, Verification and Reliability, 31(5), p. e1728, 2021. https://doi.org/10.1002/stvr.1728   DOI
15 B. Rashidi and Q. Zhao, "Autonomous root-cause fault diagnosis using symbolic dynamic based causality analysis," Neurocomputing, vol. 401, pp. 10-27, 2020. https://doi.org/10.1016/j.neucom.2020.03.007   DOI
16 C. I. Pinzon, J. Bajo, J. F. De Paz, and J. M. Corchado, "S-MAS: An adaptive hierarchical distributed multi-agent architecture for blocking malicious SOAP messages within Web Services environments," Expert Systems with Applications, vol. 38, no. 5, pp. 5486-5499, 2011. http://dx.doi.org/10.1016/j.eswa.2010.10.088   DOI
17 G. Canfora and M. Di Penta, "Testing services and service-centric systems: Challenges and opportunities," IT Professional, vol. 8, no. 2, pp. 10-17, 2006. https://doi.org/10.1109/MITP.2006.51   DOI
18 P. Huang, X. Ma, D. Shen, and Y. Zhou, "Performance regression testing target prioritization via performance risk analysis," in Proceedings of the 36th International Conference on Software Engineering, pp. 60-71, 2014. https://doi.org/10.1145/2568225.2568232   DOI
19 S. Elbaum, A. G. Malishevsky, and G. Rothermel, "Test case prioritization: A family of empirical studies," IEEE transactions on software engineering, vol. 28, no. 2, pp. 159-182, 2002. https://doi.org/10.1109/32.988497   DOI
20 S. Ghaith, M. Wang, P. Perry, Z. M. Jiang, P. O'Sullivan, and J. Murphy, "Anomaly detection in performance regression testing by transaction profile estimation," Software Testing, Verification and Reliability, vol. 26, no. 1, pp. 4-39, 2016. http://dx.doi.org/10.1002/stvr.1573   DOI
21 Oloieri, A. and P. Diac, Throughput-based Service Composition. Procedia Computer Science, 192, p. 1092-1101, 2021. https://doi.org/10.1016/j.procs.2021.08.112   DOI
22 Bohmer, K. and S. Rinderle-Ma. Multi instance anomaly detection in business process executions. in International Conference on Business Process Management. 2017. Springer. http://dx.doi.org/10.1007/978-3-319-65000-5_5   DOI
23 Kuang, K., et al., Causal inference. Engineering, 6(3): p. 253-263, 2020. https://doi.org/10.1016/j.eng.2019.08.016   DOI
24 El-Shamy, A.M., et al., Anomaly detection and bottleneck identification of the distributed application in cloud data center using software-defined networking. Egyptian Informatics Journal, 2021. https://doi.org/10.1016/j.eij.2021.01.001   DOI
25 Xu, L. Deep bidirectional intelligence: AlphaZero, deep IA-search, deep IA-infer, and TPC causal learning. in Applied Informatics, Springer, 2018. https://doi.org/10.1186/s40535-018-0052-y   DOI
26 Wu, L., et al. Causal Inference Techniques for Microservice Performance Diagnosis: Evaluation and Guiding Recommendations. in ACSOS 2021-2nd IEEE International Conference on Autonomic Computing and Self-Organizing Systems, 2021. https://hal.archives-ouvertes.fr/hal-03323055
27 R. Peng, Y.-F. Li, J.-G. Zhang, and X. Li, "A risk-reduction approach for optimal software release time determination with the delay incurred cost," International Journal of Systems Science, vol. 46, no. 9, pp. 1628-1637, 2015. https://doi.org/10.1080/00207721.2013.827261   DOI
28 U. Sivaji and P. S. Rao, "Test case minimization for regression testing by analyzing software performance using the novel method," Materials Today: Proceedings, 2021. https://doi.org/10.1016/j.matpr.2021.01.882   DOI
29 A. Ghourabi, T. Abbes, and A. Bouhoula, "Experimental analysis of attacks against web services and countermeasures," in Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services, pp. 195-201, 2010. http://dx.doi.org/10.1145/1967486.1967519   DOI
30 M. Tang, Y. Jiang, J. Liu, and X. Liu, "Location-aware collaborative filtering for QoS-based service recommendation," in 2012 IEEE 19th international conference on web services, IEEE, pp. 202-209, 2012. https://doi.org/10.1109/ICWS.2012.61   DOI