Abstract
In order to better perform fault diagnosis on web services and help users to accurately detect service faults, this paper proposes a web service fault diagnosis model based on anomaly detection (ADWSFD). At present, the outlier detection integration framework of embedded feature selection, which combines outlier scoring and feature selection, plays an important role in detecting outlier performance. We use this framework to score failures of the services participating in the experiment. Specifically, by unifying the attribute selection and fault scoring into a loss function of pairwise comparison and sorting, a reliable service attribute subset is established, and failure scoring of services based on it. In order to improve the reliability of the model, we propose to use a self-paced learning algorithm to achieve service attribute weighting. On this basis, we use the distance-based service fault scoring method to judge its impact on service fault diagnosis, and the validity of the web service fault diagnosis model based on anomaly detection is verified through experimental analysis.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, T., et al.: Workflow-aware automatic fault diagnosis for microservice-based applications with statistics. IEEE Trans. Netw. Service Manag. (99), 1–1 (2020)
Bui, K.T., et al.: A fault detection and diagnosis approach for multi-tier application in cloud computing. J. Commun. 22(5), 399–414 (2020)
Li, P., et al.: Fault localization with weighted test model in model transformations. IEEE Access (99), 1–1 (2020)
Wang, G., Lin, X., Hendler, J., Song, W., Xu, Z., Liu, G. (eds.): WISA 2020. LNCS, vol. 12432. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60029-7
Kang, Z., et al.: Robust graph learning from noisy data. IEEE Trans. Cybern. (2018)
Kang, Z., et al.: Similarity learning via kernel preserving embedding. Proc. AAAI Conf. Artif. Intell. 33, 4057–4064 (2019)
Cheng, L., et al.: Outlier detection ensemble with embedded feature selection. AAAI-20 3503–3512 (2020)
Pawan, M., et al.: Self-paced learning for latent variable models. In NIPS 1189–1197 (2011)
Hjx, A., Wtl, A.J.I.F.: Robust AdaBoost based ensemble of one-class support vector machines. Inf. Fusion 55, 45–58 (2020)
Cai, Z., et al.: A real-time trace-level root-cause diagnosis system in alibaba datacenters. IEEE Access (99), 1–1 (2019)
Zahid, M.A., et al.: Collaborative business process fault resolution in the services cloud. IEEE Trans. Serv. Comput. (99), 1–1 (2021)
Ghasedi, K., et al.: Balanced self-paced learning for generative adversarial clustering network. IEEE/CVF CVPR (2020)
Pang, G., et al.: LeSiNN: detecting anomalies by identifying least similar nearest neighbours. In: IEEE International Conference on Data Mining Workshop (2016)
Song, H., Chen, Z.-c.: Multi-attribute decision-making method based distance and COPRAS method with probabilistic hesitant fuzzy environment. Int. J. Comput. Intell. Syst. 14(1) (2021)
Pang, G., et al.: sparse modeling-based sequential ensemble learning for effective outlier detection in high-dimensional numeric data. In: Proceedings of Thirty-Second AAAI Conference on Artificial Intelligence (2017)
Zhang, C., Zhuang, C., Zheng, X., Cai, R., Li, M.: Stochastic model predictive control approach to autonomous vehicle lane keeping. Journal of Shanghai Jiaotong University (Science) 26(5), 626–633 (2021). https://doi.org/10.1007/s12204-021-2352-y
Zhang, Q., Ren, F.: Prior-based bayesian pairwise ranking for one-class collaborative filtering. Neurocomputing 440, 365–374 (2021). https://doi.org/10.1016/j.neucom.2021.01.117
Wang, F., et al.: Unsupervised soft-label feature selection. Knowl.-Based Syst. 219(2), 106847 (2021)
Duc, K.T., et al.: Nonparametric estimation of ROC surfaces under verification bias. Revstat Stat. J. 18(5), 697–720 (2020)
Acknowledgements
This paper is partially supported by the National Natural Science Foundation of China under Grant No. 62172057 and No. 61972053, The Project is sponsored by “Liaoning BaiQianWan Talents Program”under Grant No.2021921024.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Xi, YM., Jia, ZC., Diao, FX., Liu, YS., Xing, X. (2022). Fault Diagnosis of Web Services Based on Feature Selection. In: Zhao, X., Yang, S., Wang, X., Li, J. (eds) Web Information Systems and Applications. WISA 2022. Lecture Notes in Computer Science, vol 13579. Springer, Cham. https://doi.org/10.1007/978-3-031-20309-1_26
Download citation
DOI: https://doi.org/10.1007/978-3-031-20309-1_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20308-4
Online ISBN: 978-3-031-20309-1
eBook Packages: Computer ScienceComputer Science (R0)