Backward Inference in Bayesian Networks for Distributed Systems Management

Ding, Jianguo; Krämer, Bernd J.; Bai, Yingcai; Chen, Hansheng

doi:10.1007/s10922-005-9003-8

Cited by 16 publications

(7 citation statements)

References 25 publications

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“…Although Bayesian network structure can be created by experts based on domain knowledge (Ding et al , 2005), more researches are interested in learning Bayesian network from data automatically to find the structure that is most suitable to training data. Learning structure is more crucial part of the whole course and the final results are directly related with it.…”

Section: Research Model and Methodologymentioning

confidence: 99%

“…It applies two Bayesian inference algorithms that calculate belief‐updating and most‐probable‐explanation queries in singly connected belief networks to perform fault localization in belief networks with loops. As structure learning is the main issue when using Bayesian network method, more researches induce automatically learning Bayesian network from data despite instead of manual designed model based on domain knowledge (Ding et al , 2005), which may be disputed as it is unalterable and unable to reflect to the real‐time changes of data.…”

Section: Literature Reviewmentioning

confidence: 99%

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Problem localization using probabilistic dependency analysis for automated system management in ubiquitous computing

2009

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PurposeThis paper seeks to develop an approach to problem localization and an algorithm to address the issue of determining the dependencies among system metrics for automated system management in ubiquitous computing systems.Design/methodology/approachThis paper proposes an approach to problem localization for learning the knowledge of dynamic environment using probabilistic dependency analysis to automatically determine problems. This approach is based on Bayesian learning to describe a system as a hierarchical dependency network, determining root causes of problems via inductive and deductive inferences on the network. An algorithm of preprocessing is performed to create ordering parameters that have close relationships with problems.FindingsThe findings show that using ordering parameters as input of network learning, it reduces learning time and maintains accuracy in diverse domains especially in the case of including large number of parameters, hence improving efficiency and accuracy of problem localization.Practical implicationsAn evaluation of the work is presented through performance measurements. Various comparisons and evaluations prove that the proposed approach is effective on problem localization and it can achieve significant cost savings.Originality/valueThis study contributes to research into the application of probabilistic dependency analysis in localizing the root cause of problems and predicting potential problems at run time after probabilities propagation throughout a network, particularly in relation to fault management in self‐managing systems.

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Section: Research Model and Methodologymentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Problem localization using probabilistic dependency analysis for automated system management in ubiquitous computing

2009

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“…Recently, more researches are induced to learn Bayesian network from data automatically despite that Bayesian network structure can be created by experts based on domain knowledge [11], which is expensive in terms of time and cost, and also manual designed model may be disputed as it is unalterable and unable to reflect to the real-time changes of data. However, most of them are difficult to be carried out in complex domain that should consider a great number of factors, which brings overfitting problem that is one of the main issues in using machine learning.…”

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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“…Consider the backward inference in DBNs, the dynamic changes in individual nodes or individual dependencies may propagate to the whole DBN and thus cause the modification of the strongest dependent routes and the rank of the dependent sequence in causal nodes. Related research about the evidence propagation and probabilistic inference based on Bayesian networks for distributed systems management can be found in [4].…”

Section: ) Single Factor (Variable) Prediction In Dbnsmentioning

confidence: 99%

Prediction Strategies for Proactive Management in Dynamic Distributed Systems

Ding

Jiang

et al.

International Conference on Digital Telecommunications (ICDT'06)

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In real-life distributed systems, dynamic changes are unavoidable properties because of the degeneration or improvement in system performance. Hence to understand the dynamic changes and to "catch the trend" of the changes in distributed systems will be very important for distributed systems management. In order to model the dynamic changes in distributed systems, temporal extensions of Bayesian networks are employed to address the temporal factors and to model the dynamic changes of managed entities and the dependencies between them. Furthermore, the prediction capabilities are investigated by means of the relevant inference techniques when the imprecise and dynamic management information occurs in the distributed system.

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Backward Inference in Bayesian Networks for Distributed Systems Management

Cited by 16 publications

References 25 publications

Problem localization using probabilistic dependency analysis for automated system management in ubiquitous computing

Problem localization using probabilistic dependency analysis for automated system management in ubiquitous computing

Fault Localization for Self-Managing Based on Bayesian Network

Prediction Strategies for Proactive Management in Dynamic Distributed Systems

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