Exploiting functional connectivity inference for efficient root cause analysis

Winchester, Giles, Parisis, Georgios and Berthouze, Luc (2022) Exploiting functional connectivity inference for efficient root cause analysis. IEEE/IFIP Network Operations and Management Symposium, Budapest, Hungary, 25 Apr 2022 - 29 Apr 2022. Published in: IEEE/IFIP NOMS 2022 Conference Proceedings. IEEE (Accepted)

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A crucial step in remedying faults within network infrastructure is to determine their root cause. However, the large-scale, complex and dynamic nature of modern architecture makes root cause analysis challenging.
Statistical approaches for causal inference are promising, however, their deployment has been historically limited due to their high time complexity. In this paper we propose a general framework for leveraging the concept of functional connectivity to reduce the computational overhead of causal inference algorithms. We demonstrate on synthetic data that our approach can achieve substantial speedups when combined with state-of-the-art causal discovery algorithms, with only a small cost in terms of loss of causal information in some cases.

Item Type: Conference Proceedings
Additional Information: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Schools and Departments: School of Engineering and Informatics > Informatics
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Date Deposited: 16 Mar 2022 07:59
Last Modified: 16 Mar 2022 11:41
URI: http://sro.sussex.ac.uk/id/eprint/104881

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