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Backward Inference in Bayesian Networks for Distributed Systems Management

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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.

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ACKNOWLEDGMENTS

This research is part of international quality networks (IQN) project and is supported by DAAD (the German Academic Exchange Service). The authors would like to thank Carsten Schippang for providing the sample data of the campus network of FernUniversität Hagen for a whole year. Also many thanks are due to the anonymous reviewers for their valuable comments.

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Authors and Affiliations

  1. Software Engineering Institute, East China Normal University, Shanghai, 200062, P. R. China

    Jianguo Ding

  2. Department of Electrical Engineering and Information Engineering, FernUniversität Hagen, Hagen, Germany

    Bernd Krämer

  3. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, P. R. China

    Yingcai Bai

  4. East-china Institute of Computer Technology, Shanghai, P. R. China

    Hansheng Chen

Authors
  1. Jianguo Ding
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  2. Bernd Krämer
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  3. Yingcai Bai
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  4. Hansheng Chen
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Correspondence to Jianguo Ding.

Additional information

Jianguo Ding received his M. Sc. in computer science from Hefei University of Technology, P. R. China, in 1999. He obtained a joint Ph.D. in computer science between Shanghai Jiao Tong University in P. R. China and FernUniversität Hagen in Germany in 2005. He was supported by DAAD (the German Academic Exchange Service) scholarship. He is a member of the IEEE. His current research interests include distributed systems management, intelligent technology and probabilistic reasoning.

Bernd Krämer is a full professor at FernUniversität in Hagen, Germany. He obtained his diploma and doctorate in computer science from the Technical University of Berlin. He is the president of the international Society for Process and Design Sciences. His research interests include distributed software engineering, e-learning technology, distributed systems management, and dependable software.

Yingcai Bai graduated from Tsinghua University, P. R. China. He is a full professor at Shanghai Jiao Tong University, P. R. China. He is also the president of Shanghai Engineering Center of GOLDEN Network and the president of Shanghai Computer Open System Association. His research interests include network architecture, network security, and distributed systems management.

Hansheng Chen graduated in mathematics from Fudan University, P. R. China. He is a professor at East-China Institute of Computer Technology. He is a visiting professor at FernUniversität in Hagen, Germany. His research focuses on software engineering and distributed systems.

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Ding, J., Krämer, B., Bai, Y. et al. Backward Inference in Bayesian Networks for Distributed Systems Management. J Netw Syst Manage 13, 409–427 (2005). https://doi.org/10.1007/s10922-005-9003-8

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