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Creating a knowledge database on system dependability and resilience

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper deals with the problem of creating a knowledge database on system dependability and resilience, created on the basis of available system and application logs. Special to ols to collect and analyse these data from many systems have been developed. Taking into account a wide spectrum of various logs we explore them locally and globally. This allowed for identification of characteristics of normal operation and anomalous behaviour. A lot of attention is paid to the problem of selecting measures to identify symptoms characterising system operation and their usefulness in dependability and resilience evaluation or prediction. The concepts presented are illustrated with experience gained during monitoring of real systems.
Rocznik
Strony
287--307
Opis fizyczny
Bibliogr. 28 poz., il. wykr.
Twórcy
autor
  • Institute of Computer Science, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warszawa, Poland
autor
  • Institute of Computer Science, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warszawa, Poland
Bibliografia
  • 1. BRANDT J., DEBUSSCHERE B., GENTILE A., MAYO, J. and PEBAY P. (2008) Using probabilistic characterization to reduce runtime faults in HPC systems. Proc. 8th IEEE Int. Symposium on Cluster Computing and the Grid. IEEE Computer Society, 759-764.
  • 2. CHANDOLA V., BAERJEE A. and KUMAR V. (2009) Anomaly detection: A survey. ACM Computing Surveys, 41, 3, 15.1-15.58.
  • 3. CHEN CH., SINGH N. and YAJNIK M. (2012) Log analytics for dependable enterprise telephony. Proc. of 9th IEEE European Dependable Computing Conference. IEEE Computer Society, 94-101.
  • 4. CINQUE M., COTRONEO D. and PECCHIA A. (2009) A logging approach for effective dependability evaluation of computer systems. Proc. of 2nd IEEE Int. Conf. on Dependability. IEEE Computer Society, 105-110.
  • 5. FU Q., LOU J-G., WANG Y. and LI, J. (2009) Execution anomaly detection in distributed systems through unstructured log analysis. Proc. of IEEE Conference on Data Mining. IEEE Computer Society, 149-158.
  • 6. FU X., REBN R., JIANFENG Z., WEI Z., ZHEN J. and GANG L. (2012) LogMaster: mining event correlations in logs of large-scale cluster systems. Proc. of IEEE Symposium on Reliable Distributed Systems. IEEE Computer Society, 71-80.
  • 7. GMACH D., ROLIA J., CHERKASOVA L. and KEMPER A. (2007)Workload analysis and demand prediction of enterprise data center applications. Proc. of 10th IEEE Int. Symposium on IISWC. IEEE Computer Society, 171-180.
  • 8. HILL T. and LEWICKI P. (2006) Statistics: Methods and applications, A comprehensive reference for science, industry and data mining. StatSoft, Inc.
  • 9. HOFFMANN G. A., TRIVEDI K.S. and MALEK M. (2007) A best practice guide to resources forecasting for the Apache Webserver. IEEE Transactions on Reliability, 56, 4, 615-628.
  • 10. IBM (2011) IBM SPSS Modeler 14.2, Algorithms guide ftp://ftp.software. ibm.com/software/ analytics/spss/documentation/modeler/14.2/en/ AlgorithmsGuide. pdf
  • 11. JOHN L. K. and EECKHOUT L. (2006) Performance Evaluation and Benchmarking. CRC Press.
  • 12. KRÓL M. and SOSNOWSKI J. (2009) Multidimensional monitoring of computer systems. Proc. of IEEE Symp. and Workshops on Ubiquitous, Autonomic and Trusted Computing. IEEE Computer Society, 68-74.
  • 13. LATOSIŃSKI P. and SOSNOWSKI J. (2012) Monitoring dependability of a mail server. Electrical Review, 88, 10b, 223-226.
  • 14. LI X., XUE Y. and MALIN B. (2012) Detecting anomalous user behaviors in Workflow-Driven Web applications. IEEE Symposium on Reliable Distributed Systems. IEEE Computer Society, 1-10.
  • 15. LI Y., ZHENG Z. and LAN Z. (2011) Practical online failure prediction for Blue Gene/P: Period-based vs. Event-driven. Proc. of the IEEE/IFIP International Conference on Dependable Systems and Networks Workshops. IEEE Computer Society, 259-264.
  • 16. MAGALHAES J. P. and SILVA L.M. (2011) Adaptive profiling for root-cause analysis of performance anomalies in Web based applications. Proc. of IEEE International Symposium on Network Computing and Applications. IEEE Computer Society, 171-178.
  • 17. NAGGAPAN M. and VOUK M. A. (2010) Abstracting log lines to log event types for mining software system logs. Proc. of Mining Software Repositories (Co-Located with ICSE 2010). IEEE Computer Society, 114-117.
  • 18. OLINER A. and STEARLEY J. (2007) What supercomputers say: A study of five system logs. Proc. of the IEEE/IFIP Intern. Conference on Dependable Systems and Networks. IEEE Computer Society, 575-584.
  • 19. SALFINER F., LENK M. andMALEK M. (2010) A survey of failure prediction methods. ACM Computing Surveys, 42, 3, March, 10.1-10.42.
  • 20. SALFINER F. and MALEK M. (2007) Using hidden semi-Markov models for effective online failure prediction. Proc. of 26th IEEE Int. Symposium on Reliable Distributed Systems. IEEE Computer Society, 161-174.
  • 21. SIMACHE C. and KAANICHE M. (2005) Availability assessment of SunOS/ Solaris Unix systems based on syslog and wtmpx log files; a case study. Proc. of IEEE PRDC Conference. IEEE Computer Society, 49-56.
  • 22. SOSNOWSKI J. and POLESZAK M. (2006) On-line monitoring of computer systems. Proc. of IEEE DELTA Workshop. IEEE Computer Society, 327-331.
  • 23. SOSNOWSKI J. and KRÓL M. (2010) Dependability evaluation based on system monitoring. In: A. Al-Dahoud, ed., Computer Intelligence and Modern Heuristics. In-Tech, 331-348.
  • 24. SOSNOWSKI J., KUBACKI M. and KRAWCZYK H. (2012)Monitoring event logs within a cluster system. In: W. Zamojski et al., eds., Complex Systems and Dependability. Advances in Intelligent and Soft Computing, 170. Springer, 259-271.
  • 25. STOICESCU M., FABRE J. and ROY M. (2011) Architecting resilient computing systems: overall approach and open issues. In: E. A. Troubitsyna, ed., Proc. of SERENE 2011 Conference. LNCS 6968, Springer, 48-62.
  • 26. VAARANDI R. (2003) A data clustering algorithm for mining patterns from event logs. Proc. of 3rd IEEE Workshop on IP operations and Management. IEEE Computer Society, 119-126.
  • 27. VAARANDI R. (2004) A breadth-first algorithm for mining frequent patterns from event logs. INTELLCOMM 2004, LNCS 3283, Springer, 293-308.
  • 28. YE N. (2008) Secure Computer and Network Systems. John Wiley & Sons, Chichester.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b5142c79-2ebd-453b-9eca-1dbac528030b
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