Alarm correlation and fault identification in communication networks

Bouloutas, A.; Calo, Seraphin; Finkel, A.

doi:10.1109/tcomm.1994.577079

Cited by 150 publications

(86 citation statements)

References 2 publications

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“…It does not take advantage of the fact that some possible indications of the disorder have not been observed. As many researchers point out [3,24], the fact that many of its possible symptoms have not been observed should decrease our confidence in the fault's occurrence. In the realm of fault localization, an observation of network disorder is called a negative symptom.…”

Section: Analysis Of Positive Symptomsmentioning

confidence: 99%

“…When a fault propagation model is represented by a bipartite probabilistic graph, exact fault localization may be performed with the combinatorial algorithm [24], which assumes a naive approach by evaluating all possible combinations of faults with regard to their ability to explain all observed symptoms. When two or more combinations of faults are able to explain all observed symptoms, the best combination is chosen.…”

Section: Basic Conceptsmentioning

confidence: 99%

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Probabilistic fault diagnosis in communication systems through incremental hypothesis updating

Steinder

Sethi

2004

Computer Networks

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This paper presents a probabilistic event-driven fault localization technique, which uses a probabilistic symptomfault map as a fault propagation model. The technique isolates the most probable set of faults through incremental updating of a symptom-explanation hypothesis. At any time, it provides a set of alternative hypotheses, each of which is a complete explanation of the set of symptoms observed thus far. The hypotheses are ranked according to a measure of their goodness. The technique allows multiple simultaneous independent faults to be identified and incorporates both negative and positive symptoms in the analysis. As shown in a simulation study, the technique offers close-to-optimal accuracy and is resilient both to noise in the symptom data and to inaccuracies of the probabilistic fault propagation model.

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Section: Analysis Of Positive Symptomsmentioning

confidence: 99%

Section: Basic Conceptsmentioning

confidence: 99%

Probabilistic fault diagnosis in communication systems through incremental hypothesis updating

Steinder

Sethi

2004

Computer Networks

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“…There has been extensive literature on event correlation systems [24,4], mostly in the context of network management. There are also many commercial service management systems that aid problem determination, such as HP's OpenView [9], IBM's Tivoli [16], and Altaworks' Panorama [3].…”

Section: Related Workmentioning

confidence: 99%

“…Current root cause analysis techniques use approaches that do not sufficiently capture the dynamic complexity of large systems, and they require people to input extensive knowledge about the systems [24,4]. Most root cause analysis techniques, including event correlation systems, are based on static dependency models describing the relationships among the hardware and software components in the system.…”

Section: Introductionmentioning

confidence: 99%

Pinpoint: problem determination in large, dynamic Internet services

Chen

Kıcıman

Fratkin

et al.

Proceedings International Conference on Dependable Systems and Networks

525

386

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“…Those graphs are then used to return a number of fault hypotheses that best explain the observations. This has been shown to be an NP-hard problem and several heuristics have been developed to reduce the complexity of the problem [10], [11], [12]. Most of those approaches require a priori knowledge about the network and are therefore static.…”

Section: Introductionmentioning

confidence: 99%

Network events in a large commercial network: What can we learn?

Messager

Parisis

Harper³

et al. 2018

NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium

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Abstract-ISP and commercial networks are complex and thus difficult to characterise and manage. Network operators rely on a continuous flow of event log messages to identify and handle service outages. However, there is little published information about such events and how they are typically exploited. In this paper, we describe in as much detail as possible the event logs and network topology of a major commercial network. Through analysing the network topology, textual information of events and time of events, we highlight opportunities and challenges brought by such data. In particular, we suggest that the development of methods for inferring functional connectivity could unlock more of the informational value of event log messages and assist network management operators.

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Alarm correlation and fault identification in communication networks

Cited by 150 publications

References 2 publications

Probabilistic fault diagnosis in communication systems through incremental hypothesis updating

Probabilistic fault diagnosis in communication systems through incremental hypothesis updating

Pinpoint: problem determination in large, dynamic Internet services

Network events in a large commercial network: What can we learn?

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