Taming of the shrew: Modeling the normal and faulty behaviour of large-scale HPC systems

Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium, IPDPS 2012

Volumn , Issue , 2012, Pages 1168-1179

  Gainaru, Ana ^a   Cappello, Franck ^a,b   Kramer, William ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^b INRIA   (France)

Author keywords

fault detection; fault tolerance; large scale HPC systems; signal analysis

Indexed keywords

COMPLEX MACHINES; ENTIRE SYSTEM; EVENT ANALYSIS; EVENT MINING; FAILURE RATE; FAULT PREDICTION; FILTERING ALGORITHM; HARDWARE AND SOFTWARE COMPONENTS; NORMAL FLOW; PROACTIVE FAULT; SIGNAL ANALYZERS; SYSTEM ADMINISTRATION; SYSTEM OUTPUT; SYSTEM STATE;

DISTRIBUTED PARAMETER NETWORKS; FAULT DETECTION; FAULT TOLERANCE;

SIGNAL ANALYSIS;

EID: 84866885057     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IPDPS.2012.107     Document Type: Conference Paper

References (29)

1. Y. Liang: BlueGene/L Failure Analysis and Prediction Models. Proceedings of the International Conference on Dependable Systems and Networks, 2006
2. N. Taerat et al: BlueGene/L Log Analysis and Time to Interrupt Estimation. International Conference on Availability, Reliability and Security, pp.173-180, 2009
3. R. K. Sahoo et al: Critical Event Prediction for Proactive Management In Large-scale Computer Clusters. International conference on Knowledge discovery and data mining, pp 426-435, 2003
4. N. Yigitbasi et al: Analysis and Modeling of Time-Correlated Failures in Large-Scale Distributed Systems. IEEE/ACM International Conference on Grid Computing, pp 65-72, 2010
5. J. G. Lou et al: Mining Dependency in Distributed Systems through Unstructured Logs Analysis ACM SIGOPS Volume 44 Issue 1, January 2010
6. W. Xu et al: Online System Problem Detection by Mining Patterns of Console Logs IEEE International Conference on Data Mining, pp 588-597, 2009
7. Z. Zheng et al: A Practical Failure Prediction with Location and Lead Time for Blue Gene/P International Conference on Dependable Systems and Networks Workshops, pp 15-222010
8. J. Gu et al: Dynamic Meta-Learning for Failure Prediction in Large-Scale Systems: A case Study International Conference on Parallel Processing, pp 157-164, 2008
9. A. Gainaru et al: Adaptive Event Prediction Strategy with Dynamic Time Window for Large-Scale HPC Systems. 2011
10. R. Ren et al: LogMaster: Mining Event Correlations in Logs of Large-scale Cluster Systems CoRR abs/1003.0951, 2010
11. N. Nakka et al: Predicting Node Failure in High Performance Computing Systems from Failure and Usage Logs IEEE Workshop on Dependable Parallel, Distributed and Network-Centric Systems, 2011
12. A. Gainaru, F. Cappello et al: Event log mining tool for large scale HPC systems. Euro-Par conference, Sep 2011.
13. N. Taerat et al: Blue gene/l log analysis and time to interrupt estimation. International Conference on Availability, Reliability and Security, pp 173-180 (2009).
14. R. Iyer et al: Automatic recognition of intermittent failures: An experimental study of field data. IEEE Transactions on Computers, 39:525537, 1990.
15. A. Pecchia et al: Improving Log-Based Field Failure Data Analysis of Multi-Node Computing Systems DNS 2011
16. Z. Zheng, Z. Lan, B. Park, and A. Geist. System log preprocessing to improve failure prediction. International Conference on Dependable Systems and Networks, 2009.
17. W. Waters et al: Bandpass Signal Sampling and Coherent Detection IEEE Transactions on Aerospace and Electronic Systems, Nov. 1982
18. A. Oliner et al: What Supercomputers Say: A Study of Five System Logs. International Conference on Dependable Systems and Networks, June, 2007
19. E. Heien et al: Modeling and Tolerating Heterogeneous Failures in Large Parallel Systems - Supercomputing 2011, November 2011
20. B. Schroeder et al: A large-scale study of failures in high-performance computing systems. IEEE DSN, pages 249-258, June 2006
21. H. Posten: The robustness of the twosample ttest over the Pearson system. Journal of Statistical Computation and Simulation, Volume 6, Issue 3-4, 1978
22. National Center for Supercomputing Applications at the University of Illinois. www.ncsa.illinois.edu. Accessed on 2010.
23. G. K. Varshney: An Efficient Methodology for Noise Characterization. International Conference on VLSI Design, Pages 330-335, 2005
24. B. Goossens et al: Removal of Correlated Noise by Modeling the Signal of Interest in the Wavelet Domain. IEEE Trans Image Process. 2009
25. Z. Zheng, Y. Li, Z. Lan. Anomaly Localization in Large-Scale Clusters. 2007.
26. E. Kcman and L. Subramanian. Root Cause Localization in Large Scale Systems. 2005.
27. The computer failure data repository. http://cfdr.usenix.org. Accessed on 2010.
28. B. Vidakovic and P. Muller: Wavelets for Kids, unpublished, 1994, http://www.diku.dk/ jda, Accessed on 2011
29. L. B. Gomez et al: FTI: high performance Fault Tolerance Interface for hybrid systems. International Conference for High Performance Computing, Networking, Storage and Analysis, 2011.