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Towards an approximate graph entropy measure for identifying incidents in network event data
conference contribution
posted on 2023-06-09, 00:25 authored by Philip Tee, George ParisisGeorge Parisis, Ian WakemanIan WakemanA key objective of monitoring networks is to identify potential service threatening outages from events within the network before service is interrupted. Identifying causal events, Root Cause Analysis (RCA), is an active area of research, but current approaches are vulnerable to scaling issues with high event rates. Elimination of noisy events that are not causal is key to ensuring the scalability of RCA. In this paper, we introduce vertex-level measures inspired by Graph Entropy and propose their suitability as a categorization metric to identify nodes that are a priori of more interest as a source of events. We consider a class of measures based on Structural, Chromatic and Von Neumann Entropy. These measures require NP-Hard calculations over the whole graph, an approach which obviously does not scale for large dynamic graphs that characterise modern networks. In this work we identify and justify a local measure of vertex graph entropy, which behaves in a similar fashion to global measures of entropy when summed across the whole graph. We show that such measures are correlated with nodes that generate incidents across a network from a real data set.
History
Publication status
- Published
File Version
- Accepted version
Journal
Proceedings of the NOMS 2016 IEEE/IFIP Network Operations and Management Symposium 2016; Istanbul, Turkey; 25-29 April 2016ISSN
2374-9709Publisher
Institute of Electrical and Electronics EngineersExternal DOI
Page range
1049-1054ISBN
9871509002238Department affiliated with
- Informatics Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2016-03-02First Open Access (FOA) Date
2016-03-02First Compliant Deposit (FCD) Date
2016-03-01Usage metrics
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