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Backward inference in Bayesian networks for distributed systems management

Journal of Network and systems Management 2005年第4期13卷 409-427页

作者： Ding, Jianguo Krämer, Bernd Bai, Yingcai Chen, Hansheng Software Engineering Institute East China Normal University Shanghai 200062 China Department of Electrical Engineering and Information Engineering FernUniversität Hagen Germany Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China East-china Institute of Computer Technology Shanghai China IEEE FernUniversität Hagen Germany International Society for Process and Design Sciences Shanghai Jiao Tong University China Shanghai Engineering Center of GOLDEN Network Shanghai Computer Open System Association East-China Institute of Computer Technology

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 information, uncertain situations, and dynamic changes. In this paper, Bayesian networks are proposed to model dependencies between managed objects in distributed systems. The strongest dependency route (SDR) algorithm is developed for backward inference in Bayesian networks. The SDR algorithm can track the strongest causes and trace the strongest routes between particular effects and its causes, the strongest dependency of causes can be also achieved by the algorithm. Thus, the backward inference provides an efficient mechanism in fault locating, and is beneficial for performance management. © 2005 Springer Science+Business Media, Inc.

关键词： computer networks

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