Abstract
The increasing complexity of Ubiquitous computing leads to the challenges in managing systems in an automated way, which accurately identifies problems and solves them. Many Artificial Intelligent techniques are presented to support problem determination. In this paper, a mechanism for problem localization based on analyzing real-time streams of system performance for automated system management is proposed. We use Bayesian network to construct a compact network and provide both inductive and deductive inferences through probabilistic dependency analysis throughout the network. An algorithm for extracting a certain factors that are highly related to problems is introduced, which supports network learning in diverse domains. The approach enables us to both diagnose problems on the underlying system status and predict potential problems at run time via probabilities propagation throughout network. A demonstration focusing on system reliability in distributed system management is given to prove the availability of proposed mechanism, and thereby achieving self-managing capability.
This work was supported in parts by Ubiquitous Autonomic Computing and Network Project, 21th Century Frontier R&D Program, MIC, Korea, ITRC IITA-2006-(C1090-0603-0046), Grant No. R01-2006-000-10954-0, Basic Research Program of the Korea Science &Engineering Foundation, and the Post-BK21 Project.
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Piao, S., Park, J., Lee, E. (2007). Problem Localization for Automated System Management in Ubiquitous Computing. In: Denko, M.K., et al. Emerging Directions in Embedded and Ubiquitous Computing. EUC 2007. Lecture Notes in Computer Science, vol 4809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77090-9_15
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DOI: https://doi.org/10.1007/978-3-540-77090-9_15
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