Fault Localization for Self-Managing Based on Bayesian Network
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Fault localization plays a significant role in enormous distributed system because it can identify root cause of observed faults automatically, supporting self-managing which remains an open topic in managing and controlling complex distributed systems to improve system reliability. Although many Artificial Intelligent techniques have been introduced in support of fault localization in recent research especially in increasing complex ubiquitous environment, the provided functions such as diagnosis and prediction are limited. In this paper, we propose fault localization for self-managing in performance evaluation in order to improve system reliability via learning and analyzing real-time streams of system performance events. We use probabilistic reasoning functions based on the basic Bayes’ rule to provide effective mechanism for managing and evaluating system performance parameters automatically, and hence the system reliability is improved. Moreover, due to large number of considered factors in diverse and complex fault reasoning domains, we develop an efficient method which extracts relevant parameters having high relationships with observing problems and ranks them orderly. The selected node ordering lists will be used in network modeling, and hence improving learning efficiency. Using the approach enables us to diagnose the most probable causal factor with responsibility for the underlying performance problems and predict system situation to avoid potential abnormities via posting treatments or pretreatments respectively. The experimental application of system performance analysis by using the proposed approach and various estimations on efficiency and accuracy show that the availability of the proposed approach in performance evaluation domain is optimistic.
Abstract
Fault localization plays a significant role in enormous distributed system because it can identify root cause of observed faults automatically, supporting self-managing which remains an open topic in managing and controlling complex distributed systems to improve system reliability. Although many Artificial Intelligent techniques have been introduced in support of fault localization in recent research especially in increasing complex ubiquitous environment, the provided functions such as diagnosis and prediction are limited. In this paper, we propose fault localization for self-managing in performance evaluation in order to improve system reliability via learning and analyzing real-time streams of system performance events. We use probabilistic reasoning functions based on the basic Bayes’ rule to provide effective mechanism for managing and evaluating system performance parameters automatically, and hence the system reliability is improved. Moreover, due to large number of considered factors in diverse and complex fault reasoning domains, we develop an efficient method which extracts relevant parameters having high relationships with observing problems and ranks them orderly. The selected node ordering lists will be used in network modeling, and hence improving learning efficiency. Using the approach enables us to diagnose the most probable causal factor with responsibility for the underlying performance problems and predict system situation to avoid potential abnormities via posting treatments or pretreatments respectively. The experimental application of system performance analysis by using the proposed approach and various estimations on efficiency and accuracy show that the availability of the proposed approach in performance evaluation domain is optimistic.
참고문헌 (17)
- R. K. Sahoo, "Critical event prediction for proactive management in large-scale computer clusters", In Proceedings of the ACM SIGKDD, Intl. Conf. on Knowledge Discovery and Data Mining, pp.426~435 (2003)
- Jeffrey O, "The Vision of Autonomic Computing", IEEE Computer Society (2003)
- Irina Rish, "Adaptive Diagnosis in Distributed Systems", IEEE Transactions on Neural Networks (2005)
- Yuan-Shun Dai, "Autonomic Computing and Reliability Improvement", Proceedings of Eighth IEEE International Symposium on Object-Oriented Real-Time Distributed Computing (ISORC’05), pp.204~206 (2005)
- "IBM Self-Aware Distributed Systems"[URL]
- "Sun Microsystems: Predictive Self-Healing in the Solaris 10 Operating System"[URL]
- Bhaskara Reddy Moole, "Enterprise web site problem diagnosis using Bayesian Belief Networks", outheastCon, Proceedings, IEEE, pp.284~396 (2005)
- Self-Aware Services- Using Bayesian Networks for Detecting Anomalies in Internet-based ServicesHP Labs Technical Reports HPL-2001-23R1, "J.Bronstein", ~ (2001)
- Rui Zhang, "Performance Problem Localization in Self-Healing, Service-Oriented Systems using Bayesian Networks", Proceedings of the 2007 ACM symposium on Applied computing, pp.104~109 (2007)
- Malgorzata Steinder, "Probabilistic Fault Localization in Communication Systems Using Belief Networks", IEEE, 2004, 809~822
- Jianguo Ding, "Backward inference in Bayesian networks for distributed systems management", Journal of Network and Systems Management, 13(4) (2005)
- Ethem Alpaydm, "Introduction of Machine Learning. Massachusetts Institute of Technology", 39~60 (2004)
- "Charles River Analytics Inc, About Bayesian Belief Networks, Charles River Analytics, Inc", ~ (2004)
- Jie Cheng, "An algorithm for Bayesian Belief Network construction from Data", In Proceedings of AI &STAT’, pp.83~90 (1997)
- Cheng, J, "Learning Bayesian Networks from Data: An Efficient Approach Based on Information Theory", In Proceedings of the sixth ACM International Conference on Information and Knowledge Management (1997)
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