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) |
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 |
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