Abstract
In large-scale cloud service datacenters, there always have a monitoring center in charge of the health status of all system components. When faults occur, it should react rapidly and notify managers to avoid further lose. The most popular solution for fault detection in enterprise environments is rule-based detection. And to our knowledge, there exists a limitation for the existing rule-based solution that rules are always configured by managers relying on experiences, which is wildly inaccurate and wastes lots of labor. We present a methodology that can discover monitoring rules automatically and accurately in this paper. And through our experiment, we demonstrate it correct and effective.
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Arthur, D., Vassilvitskii, S.: k-means ++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035. Society for Industrial and Applied Mathematics (2007)
Barham, P., Isaacs, R., Mortier, R., Narayanan, D.: Magpie: online modelling and performance-aware systems. In: HotOS, pp. 85–90 (2003)
Chen, M.Y., Kiciman, E., Fratkin, E., Fox, A., Brewer, E.: Pinpoint: problem determination in large, dynamic internet services. In: DSN 2002 Proceedings.of the 2002 International Conference on Dependable Systems and Networks, pp. 595–604. IEEE (2002)
Denison, D., Mallick, B., Smith, A.: Automatic bayesian curve fitting. J. R. Stat. Soc.: Series B (Stat. Methodol.) 60(2), 333–350 (1998)
Foundation, W.: Curve fitting. http://en.wikipedia.org/wiki/Curvefitting
Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)
Id´e, T., Kashima, H.: Eigenspace-based anomaly detection in computer systems. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 440–449. ACM (2004)
Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal Mach. Intell. 24(7), 881–892 (2002)
Tan, P.-N., Steinbach, M., Kumar, V.: Cluster analysis: basic concepts and algorithms. In: Tan, P.-N., Steinbach, M., Kumar, V. (eds.) Introduction to Data Mining, pp. 487–568. Addison-Wesley, New York (2006)
Acknowledgment
This work is supported by the National Key Technology R&D Program (Grant NO. 2012BAH17FOl) and NSFC-NSF International Cooperation Project (Grant NO. 61361126011).
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Ding, C., Zeng, M., Wang, K., Pei, P., Luan, Z., Qian, D. (2015). An Active Approach for Automatic Rule Discovery in Rule-Based Monitoring Systems. In: Yueming, L., Xu, W., Xi, Z. (eds) Trustworthy Computing and Services. ISCTCS 2014. Communications in Computer and Information Science, vol 520. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47401-3_40
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DOI: https://doi.org/10.1007/978-3-662-47401-3_40
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