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Issue No.03 - Third Quarter (2012 vol.5)
pp: 345-357
Yang Song , IBM Research, Hawthrone
Anca Sailer , Microsoft Research, Redmond
Hidayatullah Shaikh , IBM Research, Hawthorne
ABSTRACT
The overwhelming amount of various monitoring and log data generated in multitier IT systems makes problem determination one of the most expensive and labor-intensive tasks in IT Services arena. Particularly the initial step of problem classification is complicated by error propagation making secondary problems surfacing on multiple dependent resources. In this paper, we propose to automate the process of problem classification by leveraging machine learning. The main focus is to categorize the problem a user experiences by recognizing the real root cause specificity leveraging available training data such as monitoring and logs across the systems. We transform the structure of the problem into a hierarchy using an existing taxonomy. We then propose an efficient hierarchical incremental learning algorithm which is capable of adjusting its internal local classifier parameters in realtime. Comparing to the traditional batch learning algorithms, this online solution decreases the computational complexity of the training process by learning from new instances on an incremental fashion. Our approach significantly reduces the memory required to store the training instances. We demonstrate the efficiency of our approach by learning hierarchical problem patterns for several issues occurring in distributed web applications. Experimental results show that our approach substantially outperforms previous methods.
INDEX TERMS
Training, Taxonomy, Training data, Monitoring, Prediction algorithms, Frequency modulation, Machine learning, services computing, Machine learning, artificial intelligence, computing methodologies, services technologies, principles of services
CITATION
Yang Song, Anca Sailer, Hidayatullah Shaikh, "Hierarchical Online Problem Classification for IT Support Services", IEEE Transactions on Services Computing, vol.5, no. 3, pp. 345-357, Third Quarter 2012, doi:10.1109/TSC.2011.3
REFERENCES
[1] IBM Trade Performance Benchmark Sample, http://www-306. ibm.com/software/webservers/ appserv/wasperformance.html, 2012.
[2] IBM Websphere Studio Workload Simulator, http://www-306. ibm.com/software/awdtools studioworkloadsimulator, 2012.
[3] Snappimon Monitoring Suite, http:/www.snappimon.com, 2012.
[4] M.K. Agarwal, N. Sachindran, M. Gupta, and V. Mann, "Fast Extraction of Adaptive Change Point Based Patterns for Problem Resolution in Enterprise Systems," Proc. Distributed Systems, Operations and Management (DSOM), 2006.
[5] A. Brown, G. Kar, and A. Keller, "An Active Approach to Characterizing Dynamic Dependencies for Problem Determination in a Distributed Environment," Proc. Seventh IFIP/IEEE Int'l Symp. Integrated Network Management, 2001.
[6] N. Cesa-Bianchi, C. Gentile, and L. Zaniboni, "Incremental Algorithms for Hierarchical Classification," J. Machine Learning Research, vol. 7, pp. 31-54, 2006.
[7] M.Y. Chen, E. Kiciman, E. Fratkin, A. Fox, and E. Brewer, "Pinpoint: Problem Determination in Large, Dynamic Internet Services," Proc. Int'l Conf. Dependable Systems and Networks (DSN), pp. 595-604, 2002.
[8] I. Cohen, M. Goldszmidt, A. Fox, J. Symons, J. Symons, S. Zhang, S. Zhang, T. Kelly, and T. Kelly, "Capturing, Indexing, Clustering, and Retrieving System History," Proc. Capturing, Indexing, Clustering, and Retrieving System History (SOSP '05), pp. 105-118, 2005.
[9] S. Dumais and H. Chen, "Hierarchical Classification of Web Content," Proc. 23rd Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR '00), pp. 256-263, 2000.
[10] A. Ganapathi, Y.M. Wang, N. Lao, and J.R. Wen, "Why PCs Are Fragile and What We Can Do About It: A Study of Windows Registry Problems," Proc. Int'l Conf. Dependable Systems and Networks (DSN '04), p. 561, 2004.
[11] I. Guyon and A. Elisseeff, "An Introduction to Variable and Feature Selection," J. Machine Learning Research, vol. 3, pp. 1157-1182, 2003.
[12] J. Hellerstein and V.R Tummalapalli, "Using Multidimensional Databases for Problem Determination and Planning of a Networked Application," Proc. IEEE Third Int'l Workshop Systems Management (SMW '98), p. 117, 1998.
[13] A. Kolcz and W. tau Yih, "Raising the Baseline for High-Precision Text Classifiers," Proc. 13th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '07), pp. 400-409, 2007.
[14] N. Lao, J.R Wen, W.Y. Ma, and Y.M. Wang, "Combining High Level Symptom Descriptions and Low Level State Information for Configuration Fault Diagnosis," Proc. 18th USENIX Conf. System Administration (LISA '04), pp. 151-158, 2004.
[15] M.A. Munawar and P.A.S. Ward., "A Comparative Study of Pairwise Regression Techniques for Problem Determination," Proc. Conf. Center for Advanced Studies on Collaborative Research (CASCON '07), pp. 152-166, 2007.
[16] S. Pang, S. Ozawa, and N. Kasabov, "Incremental Linear Discriminant Analysis for Classification of Data Streams," IEEE Trans. System, Man and Cybernetics, vol. 35, no. 5, pp. 905-914, Nov. 2005.
[17] L. Ralaivola and F. d'Alché-Buc, "Incremental Support Vector Machine Learning: A Local Approach," Proc. Int'l Conf. Artificial Neural Networks (ICANN '01), pp. 322-330, 2001.
[18] L. Ralaivola and F. d'Alché-Buc, "Incremental Support Vector Machine Learning: A Local Approach," Proc. Int'l Conf. Artificial Neural Networks (ICANN '01), pp. 322-330, 2001.
[19] R. Rojas, Neural Networks: A Systematic Introduction. Springer, 1996.
[20] J. Rousu, C. Saunders, S. Szedmak, and J. Shawe-Taylor, "Learning Hierarchical Multi-Category Text Classification Models," Proc. 22nd Int'l Conf. Machine Learning (ICML '05), pp. 744-751, 2005.
[21] M.E. Ruiz and P. Srinivasan., "Hierarchical Text Categorization Using Neural Networks," Information Retrieval, vol. 5, pp. 87-118, 2002.
[22] G. Salton and M.J. McGill, Introduction to Modern Information Retrieval. McGraw-Hill, 1986.
[23] D. Sculley and G.M. Wachman, "Relaxed Online Svms for Spam Filtering," Proc. 30th Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR '07), pp. 415-422, 2007.
[24] M. Steinder and A.S. Sethi, "The Present and Future of Event Correlation: A Need For End-To-End Service Fault Localization," Proc. Fifth World Multiconf. Systemics, Cybernetics, and Informatics (SCI), pp. 124-129, 2001.
[25] I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun, "Support Vector Machine Learning for Interdependent and Structured Output Spaces," Proc. 21st Int'l Conf. Machine Learning (ICML '04), p. 104, 2004.
[26] A.M. Tsvetkov, "Development of Inductive Inference Algorithms Using Decision Trees," Cybernetics and Systems Analysis, vol. 29, pp. 141-145, 1993.
[27] H.J. Wang, J.C. Platt, Y. Chen, R. Zhang, and Y.-M. Wang, "Automatic Misconfiguration Troubleshooting With Peerpressure," Proc. Sixth Conf. Symp. Opearting Systems Design and Implementation (OSDI '04), pp. 17-17, 2004.
[28] Y.-M. Wang, C. Verbowski, J. Dunagan, Y. Chen, H.J. Wang, C. Yuan, and Z. Zhang, "Strider: A Black-Box, State-Based Approach to Change and Configuration Management and Support," Proc. 17th USENIX Conf. System Administration (LISA '03), pp. 159-172, 2003.
[29] J. Weston, A. Elisseeff, B. Schölkopf, and M. Tipping, "Use of the Zero Norm with Linear Models and Kernel Methods," J. Machine Learning Research, vol. 3, pp. 1439-1461, 2003.
[30] C. Yuan, N. Lao, J.-R. Wen, J. Li, Z. Zhang, Y.-M. Wang, and W.-Y. Ma., "Automated Known Problem Diagnosis with Event Traces," SIGOPS Operating System Rev., vol. 40, no. 4 pp. 375-388, 2006.
[31] A.X. Zheng, J. Lloyd, and E. Brewer, "Failure Diagnosis Using Decision Trees," Proc. First Int'l Conf. Autonomic Computing (ICAC '04), 2004.
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