Backward Inference in Bayesian Networks for Distributed Systems Management

Authors:
Jianguo Ding;Bernd Krämer;Yingcai Bai;Hansheng Chen
Affiliations:
Software Engineering Institute, East China Normal University, Shanghai, P. R. China 200062;Department of Electrical Engineering and Information Engineering, FernUniversität Hagen, Hagen, Germany;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, P. R. China;East-china Institute of Computer Technology, Shanghai, P. R. China
Venue:
Journal of Network and Systems Management
Year:
2005

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Abstract

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.