A survey of online failure prediction methods

ACM Computing Surveys

Volumn 42, Issue 3, 2010, Pages

  Salfner, Felix ^a   Lenk, Maren ^a   Malek, Miroslaw ^a  

^a HUMBOLDT UNIVERSITY   (Germany)

Author keywords

Error; Failure prediction; Fault; Prediction metrics; Runtime monitoring

Indexed keywords

FAILURE PREDICTION; FAILURE PREDICTION METHOD; FAULT PREDICTION; PREDICTION METRICS; PROACTIVE FAULT; RELIABILITY METHODS; RUNTIME MONITORING; WIDE SPECTRUM;

ELECTRIC FAULT CURRENTS; FAILURE ANALYSIS; NETWORK COMPONENTS; SURVEYS; TAXONOMIES;

FORECASTING;

EID: 77950267881     PISSN: 03600300     EISSN: 15577341     Source Type: Journal    
DOI: 10.1145/1670679.1670680     Document Type: Review

References (125)

1. ABRAHAM, A. AND GROSAN, C. 2005. Genetic programming approach for fault modeling of electronic hardware. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC). Edinburgh, U.K., Vol.2, 1563-1569.
2. AITCHISON, J. AND DUNSMORE, I. R. 1975. Statistical Prediction Analysis. Cambridge University Press, Cambridge, U.K.
3. ALTMAN, D. G. 1991. Practical Statistics for Medical Research. CRC Press, Boca Raton, FL.
4. ALTSCHUL, S., GISH, W., MILLER, W., MYERS, E., AND LIPMAN, D. 1990. Basic local alignment search tool. J. Molec. Biol. 215, 3, 403-410.
5. ANDRZEJAK, A. AND SILVA, L. 2007. Deterministic models of software aging and optimal rejuvenation schedules. In Proceedings of the 10th IEEE/IFIP International Symposium on Integrated Network Management (IM). 159-168.
6. AVIZIENIS, A. AND LAPRIE, J.-C. 1986. Dependable computing: From concepts to design diversity. Proc. IEEE 74, 5 (May), 629-638.
7. AVIZIENIS, A., LAPRIE, J.-C., RANDELL, B., AND LANDWEHR, C. 2004. Basic concepts and taxonomy of dependable and secure computing. IEEE Trans. Dependable Secure Comput. 1, 1, 11-33.
8. BABAOGLU, O., JELASITY, M., MONTRESOR, A., FETZER, C., LEONARDI, S., VAN MOORSEL A., AND VAN STEEN, M., EDS. 2005. Self-Star Properties in Complex Information Systems. Lecture Notes in Computer Science, vol.3460. Springer-Verlag, Berlin, Germany.
9. BAI, C. G., HU, Q. P., XIE, M., AND NG, S. H. 2005. Software failure prediction based on a Markov Bayesian network model. J. Syst. Softw. 74, 3 (Feb.), 275-282.
10. BASSEVILLE, M. AND NIKIFOROV, I. 1993. Detection of Abrupt Changes: Theory and Application. Prentice Hall, Englewood Cliffs, NJ.
11. BERENJI, H., AMETHA, J., AND VENGEROV, D. 2003. Inductive learning for fault diagnosis. In Proceedings of the IEEE 12th International Conference on Fuzzy Systems (FUZZ). Vol.1.
12. BLISCHKE, W. R. AND MURTHY, D. N. P. 2000. Reliability: Modeling, Prediction, and Optimization. Probability and Statistics Series. John Wiley and Sons, New York, NY.
13. BODIK, P., FRIEDMAN, G., BIEWALD, L., LEVINE, H., CANDEA, G., PATEL, K., TOLLE, G., HUI, J., FOX, A., JORDAN, M. I., AND PATTERSON, D. 2005. Combining visualization and statistical analysis to improve operator confidence and efficiency for failure detection and localization. In Proceedings of the IEEE International Conference on Autonomic Computing (ICAC). IEEE Computer Society Press, Los Alamitos, CA, 89-100.
14. BROCKLEHURST, S. AND LITTLEWOOD, B. 1996. Techniques for prediction analysis and recalibration. In Handbook of Software Reliability Engineering, M. R. Lyu, Ed. McGraw-Hill, New York, NY, Chapter 4, 119-166.
15. BROWN, A. AND PATTERSON, D. 2001. Embracing failure: A case for recovery-oriented computing (ROC). In Proceedings of the High Performance Transaction Processing Symposium.
16. CANDEA, G., KAWAMOTO, S., FUJIKI, Y., FRIEDMAN, G., AND FOX, A. 2004. Microreboot\a technique for cheap recovery. In Proceedings of the 6th Symposium on Operating Systems Design and Implementation. 31-44.
17. CANDEA, G., KICIMAN, E., KAWAMOTO, S., AND FOX, A. 2006. Autonomous recovery in componentized Internet applications. Cluster Comput. 9, 2, 175-190.
18. CANDEA, G., KICIMAN, E., ZHANG, S., KEYANI, P., AND FOX, A. 2003. Jagr: An autonomous self-recovering application server. In Proceedings of the 5th International Workshop on Active Middleware Services (Seattle, WA).
19. CASSIDY, K. J., GROSS, K. C., AND MALEKPOUR, A. 2002. Advanced pattern recognition for detection of complex software aging phenomena in online transaction processing servers. In Proceedings of the Conference on Dependable Systems and Networks (DSN). 478-482.
20. CASTELLI, V., HARPER, R., P., H., HUNTER, S., TRIVEDI, K., VAIDYANATHAN, K., AND ZEGGERT, W. 2001. Proactive management of software aging. IBM J. Res. Develop. 45, 2 (Mar.), 311-332.
21. CAVAFY, C. P. 1992. But the wise perceive things about to happen. In Collected Poems, G. Savidis, Ed. Princeton University Press, Princeton, NJ.
22. CHEN, M., ACCARDI, A., LLOYD, J., KICIMAN, E., FOX, A., PATTERSON, D., AND BREWER, E. 2004. Path-based failure and evolution management. In Proceedings of the USENIX/ACM Symposium on Networked Systems Design and Implementation (NSDI, San Francisco, CA).
23. CHEN, M., KICIMAN, E., FRATKIN, E., FOX, A., AND BREWER, E. 2002. Pinpoint: Problem determination in large, dynamic Internet services. In Proceedings of the 2002 International Conference on Dependable Systems and Networks, IPDS track (DSN). IEEE Computer Society Press, Los Alamitos, CA, 595-604.
24. CHEN, M.-S., PARK, J. S., AND YU, P. S. 1998. Efficient data mining for path traversal patterns. IEEE Trans. Knowl. Data Eng. 10, 2, 209-221.
25. CHENG, F., WU, S., TSAI, P., CHUNG, Y., AND YANG, H. 2005. Application cluster service scheme for near-zero-downtime services. In Proceedings of the IEEE International Conference on Robotics and Automation. 4062-4067.
26. CLEVELAND, W. ET AL. 1979. Robust locally weighted regression and smoothing scatterplots. J. Amer. Statist. Assoc. 74, 368, 829-836.
27. COHEN, W. W. 1995. Fast effective rule induction. In Proceedings of the Twelfth International Conference on Machine Learning. 115-123.
28. COLEMAN, D. ANDTHOMPSON,C. 2005. Model based automation and management for the adaptive enterprise. In Proceedings of the 12th Annual Workshop of the HP OpenView University Association. 171-184.
29. CROWELL, J., SHERESHEVSKY, M., AND CUKIC, B. 2002. Using fractal analysis to model software aging. Tech. rep. West Virginia University, Lane Department of CSEE, Morgantown, WV. May.
30. CSENKI, A. 1990. Bayes predictive analysis of a fundamental software reliability model. IEEE Trans. Re-liab. 39, 2 (Jun.), 177-183.
31. DAIDONE, A., DI GIANDOMENICO, F., BONDAVALLI, A., AND CHIARADONNA, S. 2006. Hidden Markov models as a support for diagnosis: Formalization of the problem and synthesis of the solution. In Proceedings of the 25th IEEE Symposium on Reliable Distributed Systems (SRDS, Leeds, U.K.).
32. DEERWESTER, S., DUMAIS, S., FURNAS, G., LANDAUER, T., AND HARSHMAN, R. 1990. Indexing by latent semantic analysis. J. Amer. Soc. Inform. Sci. 41, 6, 391-407.
33. DENSON, W. 1998. The history of reliability prediction. IEEE Trans. Reliab. 47, 3 (Sep.), 321-328.
34. DISCENZO, F., UNSWORTH, P., LOPARO, K., AND MARCY, H. 1999. Self-diagnosing intelligent motors: a key en-abler for nextgeneration manufacturing systems. In Proceedings of the IEEE Colloquium on Intelligent and Self-Validating Sensors.
35. DOMENICONI, C., PERNG, C.-S., VILALTA, R., AND MA, S. 2002. A classification approach for prediction of target events in temporal sequences. In Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD'02), T. Elomaa, H. Mannila, and H. Toivonen, Eds. Lecture Notes in Artificial Intelligence, vol.2431. Springer-Verlag, Heidelberg, Germany, 125-137.
36. DURBIN, R., EDDY, S. R., KROGH, A., AND MITCHISON, G. 1998. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, Cambridge, U.K.
37. ELBAUM, S., KANDURI, S., AND AMSCHLER, A. 2003. Anomalies as precursors of field failures. In Proceedings of the 14th IEEE International Symposium on Software Reliability Engineering (ISSRE). 108-118.
38. FARR, W. 1996. Software reliability modeling survey. In Handbook of Software Reliability Engineering, M. R. Lyu, Ed. McGraw-Hill, New York, NY, Chapter 3, 71-117.
39. FAWCETT, T. 2004. ROC graphs: Notes and practical considerations for researchers. Mach. Learn. 31, 1-38.
40. FLACH, P. A. 2004. The many faces of ROC analysis in machine learning. Tutorial at the International Conference on Machine Learning (ICML'04). http://www.cs.bris.ac.uk/flach/ICML04tutorial/.
41. FU, S. AND XU, C.-Z. 2007. Quantifying temporal and spatial fault event correlation for proactive failure management. In Proceedings of the IEEE Symposium on Reliable and Distributed Systems (SRDS).
42. GARG, S., VAN MOORSEL, A., VAIDYANATHAN, K., AND TRIVEDI, K. S. 1998. A methodology for detection and estimation of software aging. In Proceedings of the 9th International Symposium on Software Reliability Engineering (ISSRE).
43. GROSS, K. C., BHARDWAJ, V., AND BICKFORD, R. 2002. Proactive detection of software aging mechanisms in performance critical computers. In SEW '02: Proceedings of the 27th Annual NASA Goddard Software Engineering Workshop (SEW-27'02). IEEE Computer Society Press, Los Alamitos, CA.
44. GROTTKE, M., MATIAS, R., AND TRIVEDI, K. S. 2008. The fundamentals of software aging. In Proceedings of the IEEE Workshop on Software Aging and Rejuvenation (Seattle, WA).
45. GROTTKE, M. AND TRIVEDI, K. 2007. Fighting bugs: Remove, retry, replicate, and rejuvenate. IEEE Comput. 40, 107-109.
46. GUYON, I. AND ELISSEEFF, A. 2003. An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157-1182. Special Issue on Variable and Feature Selection.
47. HAMERLY, G. AND ELKAN, C. 2001. Bayesian approaches to failure prediction for disk drives. In Proceedings of the Eighteenth International Conference on Machine Learning. Morgan Kaufmann Publishers, San Francisco, CA, 202-209.
48. HASTIE, T., TIBSHIRANI, R., AND FRIEDMAN, J. 2001. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics. Springer Verlag, Berlin, Germany.
49. HÄTÖNEN, K., KLEMETTINEN, M., MANNILA, H., RONKAINEN, P., AND TOIVONEN, H. 1996. Tasa: Telecommunication alarm sequence analyzer, or: How to enjoy faults in your network. In Proceedings of the IEEE Network Operations and Management Symposium (Kyoto, Japan). Vol.2., 520-529.
50. HELLERSTEIN, J. L., ZHANG, F., AND SHAHABUDDIN, P. 1999. An approach to predictive detection for service management. In Proceedings of the 6th IEEE International Symposium on Integrated Network Management. 309-322.
51. HO, D. W. C., ZHANG, P. A., AND XU, J. 2001. Fuzzy wavelet networks for function learning. IEEE Trans. Fuzzy Syst. 9, 1, 200-211.
52. HOFFMANN, G. A. 2004. Adaptive transfer functions in radial basis function (RBF) networks. In Proceedings of the 4th International Conference on Computational Science (ICCS 2004), M. Bubak, G. D. van Albada, P. M. A. Sloot, et al., Eds. Lecture Notes in Computer Science, vol.3037. Springer-Verlag, Berlin, Germany, 682-686.
53. HOFFMANN,G.A. 2006. Failure Prediction in Complex Computer Systems: A Probabilistic Approach. Shaker Verlag, Herzogexrath, Germany.
54. HOFFMANN, G. A. AND MALEK, M. 2006. Call availability prediction in a telecommunication system: A data driven empirical approach. In Proceedings of the 25th IEEE Symposium on Reliable Distributed Systems (SRDS), Leeds, U.K.
55. HOFFMANN, G. A., SALFNER, F., AND MALEK, M. 2004. Advanced failure prediction in complex software systems. Res. rep. 172, Department of Computer Science, Humboldt University, Berlin, Germany. www.rok.informatik.hu-berlin.de/ Members/salfner.
56. HOFFMANN, G. A., TRIVEDI, K. S., AND MALEK, M. 2006. A best practice guide to resource forecasting for the Apache Webserver. In IEEE Proceedings of the 12th International Symposium Pacific Rim Dependable Computing (PRDC). University of California, Riverside, Riverside, CA.
57. HOFFMANN, G. A., TRIVEDI, K. S., AND MALEK, M. 2007. A best practice guide to resource forecasting for computing systems. IEEE Trans. Reliab. 56, 4 (Dec.), 615-628.
58. HORN, P. 2001. Autonomic computing: IBM's perspective on the state of information technology. Tech. rep. IBM, Yorktown Heights, NY.
59. HOTELLING, H. 1933. Analysis of a complex of statistical variables into principal components. J. Educat. Psych. 24, 417-441.
60. HUGHES, G., MURRAY, J., KREUTZ-DELGADO, K., AND ELKAN, C. 2002. Improved disk-drive failure warnings. IEEE Trans. Reliab. 51, 3 (Sep.), 350-357
61. IEC: INTERNATIONAL TECHNICAL COMISSION, ED. 2002. Dependability and Quality of Service, 2nd ed. IEC, Geneva, Switzerland, Chapter 191.
62. IYER, R. K., YOUNG, L. T., AND SRIDHAR, V. 1986. Recognition of error symptoms in large systems. In Proceedings of the 1986 ACM Fall Joint Computer Conference. IEEE Computer Society Press, Los Alamitos, CA, 797-806.
63. JELINSKI, Z. AND MORANDA, P. 1972. Software reliability research. In Statistical Computer Performance Evaluation, W. Freiberger, Ed. Academic Press, New York, NY.
64. KAPADIA, N. H., FORTES, J. A. B., AND BRODLEY, C. E. 1999. Predictive application-performance modeling in a computational gridenvironment. In Procedings of the 8th International IEEE Symposium on High Performance Distributed Computing. 47-54.
65. KICIMAN, E. AND FOX, A. 2005. Detecting application-level failures in component-based Internet services. IEEE Trans. Neural Netw. 16,5, Sep. 1027-1041.
66. KORBICZ, J., KOŚCIELNY, J. M., KOWALCZUK, Z., AND CHOLEWA, W., EDS. 2004. Fault Diagnosis: Models, Artificial Intelligence, Applications. Springer-Verlag, Berlin, Germany.
67. LAL, R. AND CHOI, G. 1998. Error and failure analysis of a unix server. In Proceedings of the IEEE 3rd International High-Assurance Systems Engineering Symposium (HASE). IEEE Computer Society Press Los Alamitos, CA 232-239
68. Los Alamitos, CA, 232-239. LAPRIE, J.-C. AND KANOUN, K. 1996. Software reliability and system reliability. In Handbook of Software Reliability Engineering M. R. Lyu, Ed. McGraw-Hill, New York, NY, Chapter 2, 27-69.
69. LEANGSUKSUN, C., LIU, T., RAO, T., SCOTT, S., AND LIBBY, R. 2004. A failure predictive and policy-based high availability strategy for Linux high performance computing cluster. In Proceedings of the 5th LCI International Conference on Linux Clusters: The HPC Revolution. 18-20.
70. LEVY, D. AND CHILLAREGE, R. 2003. Early warning of failures through alarm analysis\a case study in telecom voice mail systems. In ISSRE '03: Proceedings of the 14th International Symposium on Software Reliability Engineering. IEEE Computer Society Press, Los Alamitos, CA.
71. LI, L., VAIDYANATHAN, K., AND TRIVEDI, K. S. 2002. An approach for estimation of software aging in a Web server. In Proceedings of the Intenational Symposium on Empirical Software Engineering (ISESE, Nara, Japan).
72. LIANG, Y., ZHANG, Y., SIVASUBRAMANIAM, A., JETTE, M., AND SAHOO, R. 2006. Bluegene/l failure analysis and prediction models. In Proceedings of the IEEE International Conference on Dependable Systems and Networks (DSN). 425-434.
73. LIN, T.-T. Y. AND SIEWIOREK, D. P. 1990. Error log analysis: Statistical modeling and heuristic trend analysis. IEEE Trans. Reliab. 39, 4 (Oct.), 419-432.
74. LUNZE, J. 2003. Automatisierungstechnik, 1st ed. Oldenbourg, Munich, Germany. LYU, M. R., ED. 1996. Handbook of Software Reliability Engineering. McGraw-Hill, New York, NY.
75. MANNING, C. D. AND SCHÜTZE, H. 1999. Foundations of Statistical Natural Language Processing. MIT Press, Cambridge MA.
76. MELLIAR-SMITH, P. M. AND RANDELL, B. 1977. Software reliability: The role of programmed exception handling. SIGPLAN Not. 12, 3, 95-100.
77. MENG, H., DI HOU, Y., AND CHEN, Y. 2007. A rough wavelet network model with genetic algorithm and its application to aging forecasting of application server. In Procedings of the IEEE International Conference on Machine Learning and Cybernetics. Vol.5.
78. MUNDIE, C., DE VRIES, P., HAYNES, P., AND CORWINE, M. 2002. Trustworthy computing. Tech. rep., Microsoft Corp., Redmond, WA. Oct.
79. MURRAY, J., HUGHES, G., AND KREUTZ-DELGADO, K. 2003. Hard drive failure prediction using non-parametric statistical methods. In Proceedings of ICANN/ICONIP.
80. MUSA, J. D., IANNINO, A., AND OKUMOTO, K. 1987. Software Reliability: Measurement, Prediction, Application. McGraw-Hill, New York, NY.
81. NASSAR, F. A. AND ANDREWS,D. M. 1985. A methodology for analysis offailure prediction data. In Proceedings of the IEEE Real-Time Systems Symposium. 160-166.
82. NEEDLEMAN, S. B. AND WUNSCH, C. D. 1970. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Molec. Biol. 48, 3, 443-453
83. NEVILLE, S. W. 1998. Approaches for early fault detection in large scale engineering plants. Ph.D. dissertation, University of Victoria, Victoria, B.C., Canada.
84. NING,M. H., YONG, Q., DI, H., YING, C., AND ZHONG, Z. J. 2006. Software aging prediction model based on fuzzy wavelet network with adaptive genetic algorithm. In Proceedings of the IEEE 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI). IEEE Computer Society Press, Los Alamitos, CA, 659-666.
85. PARNAS, D. L. 1994. Software aging. In Proceedings of the 16th IEEE International Conference on Software Engineering (ICSE). IEEE Computer Society Press, Los Alamitos, CA, 279-287.
86. PATTERSON, D. A., BROWN, A., BROADWELL, P., CANDEA, G., CHEN, M., CUTLER, J., ENRIQUEZ, P., FOX, A., KICIMAN, E., MERZBACHER, M., OPPENHEIMER, D., SASTRY, N., TETZLAFF, W., TRAUPMAN, J., AND TREUHAFT, N. 2002. Recovery-oriented computing (ROC): Motivation, definition, techniques, and case studies. Tech. rep. UCB//CSD-02-1175. Computer Science Department, University of California, Berkeley, Berkeley, CA. March.
87. PAWLAK, Z., WONG, S. K. M., AND ZIARKO, W. 1988. Rough sets: Probabilistic versus deterministic approach. Internat. J. Man-Mach. Stud. 29, 81-95.
88. PETTITT, A. 1977. Testing the normality of several independent samples using the anderson-darling statistic. Appl. Statist. 26, 2, 156-161.
89. PFEFFERMAN, J. AND CERNUSCHI-FRIAS, B. 2002. A nonparametric nonstationary procedure for failure prediction. IEEE Trans. Reliab. 51, 4 (Dec.), 434-442.
90. PIZZA, M., STRIGINI, L., BONDAVALLI, A., AND DI GIANDOMENICO, F. 1998. Optimal discrimination between transient and permanent faults. In Proceedings of the IEEE 3rd International High-Assurance Systems Engineering Symposium (HASE). IEEE Computer Society Press, Los Alamitos, CA, 214-223.
91. QUINLAN, J. 1990. Learning logical definitions from relations. Mach. Learn. 5, 3, 239-266.
92. QUINLAN, J. 1993. C4. 5: Programs for Machine Learning. Morgan Kaufmann, San Francisco, CA.
93. RABINER, L. R. 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77, 2 (Feb.), 257-286.
94. ROVNYAK, S., KRETSINGER, S., THORP, J., AND BROWN, D. 1994. Decision trees for real-time transient stability prediction. IEEE Trans. Power Syst. 9, 3, 1417-1426.
95. SAHNER, R. A., TRIVEDI, K. S., AND PULIAFITO, A. 1996. Performance and Reliability Analysis of Computer Systems: An Example-Based Approach Using the SHARPE Software Package (The Red Book). Kluwer Academic Publishers, Dordrecht, The Netherlands.
96. SAHOO, R. K., OLINER, A. J., RISH, I., GUPTA, M., MOREIRA, J. E., MA, S., VILALTA, R., AND SIVASUBRAMANIAM, A. 2003. Critical event prediction for proactive management in large-scale computer clusters. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). ACM Press, New York, NY, 426-435.
97. SALFNER, F. 2006. Modeling event-driven time series with generalized hidden semi-Markov models. Tech. rep. 208. Department of Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany. http://edoc.hu-berlin.de/ docviews/abstract.php?id=27653.
98. SALFNER, F. 2008. Event-based Failure Prediction: An Extended Hidden Markov Model Approach. dissertation.de\Verlag im Internet GmbH, Berlin, Germany. http://www.rok.informatik.hu-berlin. de/Members/salfner/publications/ salfner08event-based.pdf.
99. SALFNER, F., HOFFMANN, G. A., AND MALEK, M. 2005. Prediction-based software availability enhancement. In Self-Star Properties in Complex Information Systems, O. Babaoglu, M. Jelasity, A. Montresor, C. Fetzer, S. Leonardi, van Moorsel A., and M. van Steen, Eds. Lecture Notes in Computer Science, vol.3460. Springer-Verlag, Berlin, Germany.
100. SALFNER, F. AND MALEK, M. 2007. Using hidden semi-Markov models for effective online failure prediction. In Proceedings of the IEEE 26th International Symposium on Reliable Distributed Systems (SRDS).
101. SALFNER, F., SCHIESCHKE, M., AND MALEK, M. 2006. Predicting failures of computer systems: A case study for a telecommunication system. In Proceedings of the IEEE International Parallel and Distributed Processing Symposium (IPDPS, Rhodes Island, Greece).
102. SALFNER, F., TSCHIRPKE, S., AND MALEK, M. 2004. Comprehensive logfiles for autonomic systems. In Proceedings of the IEEE International Parallel and Distributed Processing Symposium (IPDPS), Workshop on Fault-Tolerant Parallel, Distributed and Network-Centric Systems (FTPDS). IEEE Computer Society Press, Los Alamitos, CA.
103. SEN, P. K. 1968. Estimates of the regression coefficient based on Kendall's tau. J. Amer. Statist. Assoc. 63, 324 (Dec.), 1379-1389.
104. SHERESHEVSKY, M., CROWELL, J., CUKIC, B., GANDIKOTA, V., AND LIU, Y. 2003. Software aging and multifrac-tality of memory resources. In Proceedings of the International Conference on Dependable Systems and Networks (DSN). IEEE Computer Society Press, Los Alamitos, CA, 721-730.
105. SIEWIOREK, D. P. AND SWARZ, R. S. 1998. Reliable Computer Systems, 3rd ed. A. K. Peters, Ltd., Wellesley, MA.
106. SINGER, R. M., GROSS, K. C., HERZOG, J. P., KING, R. W., AND WEGERICH, S. 1997. Model-based nuclear power plant monitoring and fault detection: Theoretical foundations. In Proceedings of the Conference on Intelligent System Application to Power Systems (ISAP, Seoul, Korea). 60-65.
107. SMITH, T. AND WATERMAN, M. 1981. Identification of common molecular subsequences. J. Molec. Biol. 147, 195-197.
108. SRIKANT, R. AND AGRAWAL, R. 1996. Mining sequential patterns: Generalizations and performance improvements. In Proceedings of the 5th International Conference on Extending Database Technology, EDBT, P. M. G. Apers, M. Bouzeghoub, and G. Gardarin, Eds. Lecture Notes in Computer Science, vol.1057. Springer-Verlag, Berlin, Germany, 3-17.
109. TANG, D. AND IYER, R. 1993. Dependability measurement and modeling of a multicomputer system. IEEE Trans. Comput. 42, 1 (Jan.), 62-75.
110. TROUDET, T., MERRILL, W., CENTER, N., AND CLEVELAND, O. 1990. A real time neural net estimator of fatigue life. In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN). 59-64.
111. TSAO, M. M. AND SIEWIOREK, D. P. 1983. Trend analysis on system error files. In Proceedings of the 13th International Symposium on Fault-Tolerant Computing (Milano, Italy). 116-119.
112. TURNBULL, D. AND ALLDRIN, N. 2003. Failure prediction in hardware systems. Tech. rep. University of California, San Diego, CA. http://www.cs.ucsd.edu/~dturnbul/Papers/ServerPrediction.pdf.
113. ULERICH, N. AND POWERS, G. 1988. On-line hazard aversion and fault diagnosis in chemical processes: The digraph+fault-tree method. IEEE Trans. Reliab. 37, 2 (Jun.), 171-177.
114. VAIDYANATHAN, K. AND TRIVEDI, K. S. 1999. A measurement-based model for estimation of resource exhaustion in operational software systems. In Proceedings of the International Symposium on Software Reliability Engineering (ISSRE).
115. VAN RIJSBERGEN, C. J. 1979. Information Retrieval, second ed. Butterworth, London, U.K.
116. VAPNIK, V. N. 1995. The Nature of Statistical Learning Theory. Springer Verlag, New York, NY.
117. VESELY, W., GOLDBERG, F. F., ROBERTS, N. H., AND HAASL, D. F. 1981. Fault tree handbook. Tech. rep. NUREG-0492. U.S. Nuclear Regulatory Commission, Washington, DC.
118. VILALTA, R., APTE, C. V., HELLERSTEIN, J. L., MA, S., AND WEISS, S. M. 2002. Predictive algorithms in the management of computer systems. IBM Syst. J. 41, 3, 461-474.
119. VILALTA, R. AND MA, S. 2002. Predicting rare events in temporal domains. In Proceedings of the IEEE International Conference on Data Mining (ICDM). IEEE Computer Society Press, Los Alamitos, CA, 474-482.
120. WARD, A., GLYNN, P., AND RICHARDSON, K. 1998. Internet service performance failure detection. SIGMETRICS Perform. Eval. Rev. 26, 3, 38-43.
121. WARD, A. AND WHITT, W. 2000. Predicting response times in processor-sharing queues. In Proceedings of the Fields Institute Conference on Communications Networks,P.W.Glynn,D. J.MacDonald, andS. J. Turner, Eds.
122. WEISS, G. 1999. Timeweaver: A genetic algorithm for identifying predictive patterns in sequences of events. In Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann, San Francisco, CA, 718-725.
123. WEISS, G. 2002. Predicting telecommunication equipment failures from sequences of network alarms. In Handbook of Knowledge Discovery and Data Mining, W. Kloesgen and J. Zytkow, Eds. Oxford University Press, Oxford, U.K., 891-896.
124. WONG, K. C. P., RYA N, H., AND TINDLE, J. 1996. Early warning fault detection using artificial intelligent methods. In Proceedings of the Universities Power Engineering Conference.
125. ZIPF, G. K. 1949. Human Behavior and the Principle of Least Effort: An Introduction to Human Ecology. Addison-Wesley Press, Cambridge, MA