High speed and robust event correlation

Yemini, Shaula; Kliger, S.; Mozes, E.; Yemini, Yechiam; Ohsie, D.

doi:10.1109/35.492975

Cited by 293 publications

(192 citation statements)

References 4 publications

Supporting

Mentioning

188

Contrasting

Unclassified

Order By: Relevance

“…Thanks to this feature, the algorithm continuously provides a system administrator with information about which faults are likely to exist in the system given symptoms observed thus far. In non-incremental techniques, such information is available on a periodic basis only [10,17]. The technique proposed here produces a set of alternative hypotheses rather than just a single explanation.…”

Section: Introductionmentioning

confidence: 99%

“…While relationships between faults and symptoms in real-life systems are usually more complex than may be represented by a bipartite graph (in particular, they are frequently indirect), many fault localization techniques proposed in the literature [4,10,16,17] use bipartite fault propagation models. The focus on this type of a model is justified by the following arguments: (1) Performing fault localization with more complex representations is difficult.…”

Section: Introductionmentioning

confidence: 99%

“…-Event-driven diagnosis, which avoids the inflexibility of window-based tools [1], is not prone to inaccuracies resulting from an incorrect time-window specification, and allows fault localization to be interleaved with testing. -Resilience to lost and spurious symptoms [5,8,17], which may dramatically reduce its accuracy if not taken into account by a fault localization algorithm. -High accuracy and low-polynomial computational complexity.…”

Section: Introductionmentioning

confidence: 99%

“…This paper presents a non-deterministic event-driven fault localization [9,10,17] technique, which uses a probabilistic symptom-fault map as a fault propagation model. While investigating fault localization techniques suitable for bipartite fault propagation models, this paper states the following objectives: -Usage of probabilistic reasoning, which is necessary to diagnose Byzantine problems or when relationships among system events may not be determined with certainty, e.g., due to their dynamic nature [5,6,8,10,11].…”

Section: Introductionmentioning

confidence: 99%

“…(In general, the problem is NP-hard [10].) To avoid this complexity, more detailed models are frequently reduced to bipartite ones through a sequence of graph reduction operations [17]. (2) Building more complex models requires a profound knowledge of the underlying system, while symptom-fault maps may be obtained through external observation.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Probabilistic Event-Driven Fault Diagnosis through Incremental Hypothesis Updating

Steinder

Sethi

2003

Integrated Network Management VIII

View full text Add to dashboard Cite

Abstract:A probabilistic event-driven fault localization technique is presented, which uses a symptom-fault map as a fault propagation model. The technique isolates the most probable set of faults through incremental updating of the 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 is resilient both to noise in the symptom data and to the inaccuracies of the probabilistic fault propagation model. 1

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Probabilistic Event-Driven Fault Diagnosis through Incremental Hypothesis Updating

Steinder

Sethi

2003

Integrated Network Management VIII

View full text Add to dashboard Cite

show abstract

Robust event correlation scheme for fault identification in communication networks

Chen

1999

Int. J. Commun. Syst.

View full text Add to dashboard Cite

The complexity of communication networks and the amount of information transferred in these networks have made the management of such networks increasingly difficult. Since faults are inevitable, quick detection, identification, and recovery are crucial to make the systems more robust and their operation more reliable. This paper proposes a novel event correlation scheme for fault identification in communication networks. This scheme is based on the algebraic operations of sets. The causality graph model is used to describe the cause‐and‐effect relationships between network events. For each disorder, and each manifestation, a unique prime number is assigned. The use of the greatest common devisor (GCD) makes the correlation process simple and fast. A simulation model is developed to verify the effectiveness and efficiency of the proposed scheme. From simulation results, we notice that this scheme not only identifies multiple disorders at one time but also is insensitive to noise. The time complexity of the correlation process is close to a function of n, where n is the number of observed manifestations, with order O(n2); therefore, the on‐line fault identification is easy to achieve. Copyright © 1999 John Wiley & Sons, Ltd.

show abstract