Abstract
Root cause localization within multi-dimensions is a challenging task due to its large search space within a limited time. There are a series of algorithms to handle this task, but to our knowledge, there is no evaluation system to help users analyse or optimize them according to their specific data and needs. In this paper, there are two main contributions: first, we provide a multi-dimensional evaluation system to evaluate the performance of algorithms in full aspects, which can help us comprehensively and finely analyse, compare, and choose the applicable scenario of algorithms or optimize targeted algorithms; second, we analyse and find the weakness of the SoTA algorithm RiskLoc based on our contributed evaluation system, aiming at its weakness. To tackle the issue of RiskLoc found by our evaluation system, we present PRiskLoc, an efficient and effective multi-dimensional root cause localization algorithm. We demonstrate that PRiskLoc consistently outperforms state-of-the-art baselines, especially in more challenging root cause scenarios, with the F1 improved from 0.635049 to 0.724687.
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
Similar content being viewed by others
References
Meng, W., et al.: LogAnomaly: Unsupervised detection of sequential and quantitative anomalies in unstructured logs. In: IJCAI, vol. 19, pp. 4739–4745 (2019)
Zhang, S., et al.: Rapid and robust impact assessment of software changes in large internet-based services. In: Proceedings of the 11th ACM Conference on Emerging Networking Experiments and Technologies, pp. 1–13 (2015)
Zhang, S., et al.: Funnel: assessing software changes in web-based services. IEEE Trans. Serv. Comput. 11(1), 34–48 (2016)
Bhagwan, R., et al.: Adtributor: revenue debugging in advertising systems. In: Proceedings of the 11th USENIX Conference on Networked Systems Design and Implementation, NSDI 2014, Seattle, WA, pp. 43–55. USENIX Association, USA (2014)
Li, Z., et al.: Generic and robust localization of multi-dimensional root causes. In: 2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE), pp. 47–57. IEEE (2019)
**g, P., Han, Y., Sun, J., Lin, T., Hu, Y.: AutoRoot: a novel fault localization schema of multi-dimensional root causes. In: 2021 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–7. IEEE (2021)
Kalander, M.: RiskLoc: localization of multi-dimensional root causes by weighted risk. ar**v preprint ar**v:2205.10004 (2022)
Silverman, B.W.: Density Estimation for Statistics and Data Analysis, vol. 26. CRC Press (1986)
Sun, Y., et al.: HotSpot: anomaly localization for additive KPIs with multi-dimensional attributes. IEEE Access 6, 10909–10923 (2018)
Davis, R.A., Lii, KS., Politis, D.N.: Remarks on some nonparametric estimates of a density function. In: Davis, R., Lii, KS., Politis, D. (eds.) Selected Works of Murray Rosenblatt. Selected Works in Probability and Statistics. Springer, New York (2011). https://doi.org/10.1007/978-1-4419-8339-8_13
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ding, T., Wang, Y. (2024). PRiskLoc: An Enhanced Multi-dimensional Root Cause Localization Algorithm Aided by a Fine-Grained Evaluation System. In: Zhang, M., Xu, B., Hu, F., Lin, J., Song, X., Lu, Z. (eds) Computer Applications. CCF NCCA 2023. Communications in Computer and Information Science, vol 1959. Springer, Singapore. https://doi.org/10.1007/978-981-99-8764-1_1
Download citation
DOI: https://doi.org/10.1007/978-981-99-8764-1_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8763-4
Online ISBN: 978-981-99-8764-1
eBook Packages: Computer ScienceComputer Science (R0)