Jianguo Ding

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Acknowledgements I would like to acknowledge my supervisors, Prof. not have been possible without the financial support from DAAD. Many thanks are due to Mr. Carsten Schippang for intensive discussions in distributed systems management and for providing the sample management data of the campus network of FernUniversität Hagen for the year 2003. I thank Ms.(More)
As networks grow in size, heterogeneity, and complexity of applications and network services, an efficient network management system needs to work effectively even in face of incomplete management information , uncertain situations and dynamic changes. We use Bayesian networks to model the network management and consider the probabilis-tic backward(More)
The growing complexity of distributed systems in terms of hardware components, operating system, communication and application software and the huge amount of dependencies among them have caused an increase in demand for distributed management systems. An efficient distributed management system needs to work effectively even in face of incomplete management(More)
When a complex information system is modelled by a Bayesian network the backward inference is normal requirement in system management. This paper proposes one inference algorithm in Bayesian networks, which can track the strongest causes and trace the strongest routes between particular effects and their causes. This proposed algorithm will become the(More)