Probabilistic Fault Localization in Communication Systems Using Belief Networks

Steinder, Małgorzata; Sethi, Adarshpal S.

doi:10.1109/tnet.2004.836121

Cited by 152 publications

(112 citation statements)

References 47 publications

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“…As performance problem localization is required to provide self-healing capabilities and deliver the desired quality of service (QoS) in distributed, service-oriented environments, an automated approach to identifying system elements causing performance problems was presented by building on a Bayesian network model that supports probabilistic inference among service elapsed times to end-to-end response time [9]. Bayesian reasoning techniques are applied to perform fault localization in complex communication systems while using dynamic, ambiguous, uncertain, or incorrect information about the system structure and state [10].…”

Section: Related Workmentioning

confidence: 99%

Fault Localization for Self-Managing Based on Bayesian Network

Piao¹,

Park²,

Lee³

2008

The KIPS Transactions:PartB

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Fault localization plays a significant role in enormous distributed system because it can identify root cause of observed faults automatically, supporting self-managing which remains an open topic in managing and controlling complex distributed systems to improve system reliability. Although many Artificial Intelligent techniques have been introduced in support of fault localization in recent research especially in increasing complex ubiquitous environment, the provided functions such as diagnosis and prediction are limited. In this paper, we propose fault localization for self-managing in performance evaluation in order to improve system reliability via learning and analyzing real-time streams of system performance events. We use probabilistic reasoning functions based on the basic Bayes' rule to provide effective mechanism for managing and evaluating system performance parameters automatically, and hence the system reliability is improved. Moreover, due to large number of considered factors in diverse and complex fault reasoning domains, we develop an efficient method which extracts relevant parameters having high relationships with observing problems and ranks them orderly. The selected node ordering lists will be used in network modeling, and hence improving learning efficiency. Using the approach enables us to diagnose the most probable causal factor with responsibility for the underlying performance problems and predict system situation to avoid potential abnormities via posting treatments or pretreatments respectively. The experimental application of system performance analysis by using the proposed approach and various estimations on efficiency and accuracy show that the availability of the proposed approach in performance evaluation domain is optimistic.

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Section: Related Workmentioning

confidence: 99%

Fault Localization for Self-Managing Based on Bayesian Network

Piao¹,

Park²,

Lee³

2008

The KIPS Transactions:PartB

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show abstract

“…The edge is weighted with the probability of the causal implication, pðs j j f i Þ. Following previous modeling approaches [2,17] and their justification introduced in [23], we assume a noisy-OR model of probability distribution in which alternative causes of a symptom are combined using the logical operator OR. A subset of symptoms observed by a management application is denoted by S O ¼ S N [ S P , where S N and S P are the sets of all observed negative and positive symptoms, respectively.…”

Section: Basic Conceptsmentioning

confidence: 99%

“…To avoid this complexity, more detailed models are frequently reduced to bipartite ones through a sequence of graph reduction operations [3]. Constraining an FPM to a bipartite graph, allows us to develop a fault localization algorithm whose computational complexity is an order of magnitude lower than that of a more general algorithm proposed in [21].…”

Section: Introductionmentioning

confidence: 99%

“…• Some fault localization problems may be accurately represented by bipartite symptom-fault maps (e.g., the problem of end-to-end service failure diagnosis [21,22]), thereby necessitating fault localization algorithms suitable for bipartite FPMs.…”

Section: Introductionmentioning

confidence: 99%

“…The inclusion of positive symptoms into the fault localization process may allow a more accurate fault hypothesis to be isolated [21,25].…”

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confidence: 99%

See 2 more Smart Citations

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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A Bayesian approach for control loop diagnosis with missing data

Huang

Tamayo

2009

AIChE Journal

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This article is concerned with determination of the underlying source of problematic control performance through a data-driven Bayesian approach. This approach synthesizes information from different monitoring algorithms to isolate possible problem sources. A main issue encountered in the application of the data-driven approach is the problem of missing data or missing monitor reading. By introducing the concept of missing pattern, data missing problems are classified into single and multi missing patterns. A novel method based on marginalization over underlying complete evidence matrix is proposed to circumvent missing data problems. Performance of the proposed Bayesian approach is examined through simulations as well as an industrial application example to verify its ability of information synthesis. V V C 2009 American Institute of Chemical Engineers AIChE J, 56: 179-195, 2010

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Probabilistic Fault Localization in Communication Systems Using Belief Networks

Cited by 152 publications

References 47 publications

Fault Localization for Self-Managing Based on Bayesian Network

Fault Localization for Self-Managing Based on Bayesian Network

Probabilistic fault diagnosis in communication systems through incremental hypothesis updating

A Bayesian approach for control loop diagnosis with missing data

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