1570818464 paper.pdf (2.17 MB)
Accelerating causal inference based RCA using prior knowledge from functional connectivity inference
conference contribution
posted on 2023-06-10, 04:44 authored by Giles WinchesterGiles Winchester, George ParisisGeorge Parisis, Robert Harper, Luc BerthouzeLuc BerthouzeA crucial step in remedying faults within network infrastructures is to determine their root cause. However, the large-scale, complex and dynamic nature of modern networks makes causal inference-based root cause analysis challenging in terms of scalability and knowledge drift over time. In this paper, we propose a framework that utilises the neuroscientific concept of functional connectivity– a graph representation of statistical dependencies between events– as a scalable approach to acquire and maintain prior knowledge for causal inferencebased RCA approaches in dynamic networks. We demonstrate on both synthetic and real-world data that our proposed approach can provide significant speedups to existing causal inference approaches without significant loss of accuracy. We show that, in some cases, such prior knowledge can even improve the accuracy of causal inference. Finally, we discuss the impact of the choice of user-defined parameters on causal inference accuracy and conclude that the framework can safely be deployed in the real world.
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Publication status
- Published
File Version
- Accepted version
Journal
Proceedings of the 2022 18th International Conference on Network and Service Management (CNSM)ISSN
2165-963XPublisher
IEEEExternal DOI
Event name
18th International Conference on Network and Service ManagementEvent location
Thessaloniki, GreeceEvent type
conferenceEvent date
31 October - 4 November 2022ISBN
9783903176515Department affiliated with
- Informatics Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2022-09-15First Open Access (FOA) Date
2022-09-15First Compliant Deposit (FCD) Date
2022-09-15Usage metrics
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