Advertisement

Gaining Insight from Operational Data for Automated Responses

  • Kristof Kloeckner
  • John Davis
  • Nicholas C. Fuller
  • Giovanni Lanfranchi
  • Stefan Pappe
  • Amit Paradkar
  • Larisa Shwartz
  • Maheswaran Surendra
  • Dorothea Wiesmann
Chapter
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

Facilitating autonomic behavior is largely achieved by automating routine maintenance procedures, including problem detection, determination and resolution. System monitoring provides effective means for problem detection. Coupled with automated ticket creation, it ensures that a degradation of the vital signs, defined by acceptable thresholds or known patterns, is flagged as a problem candidate. It is a known practice to define thresholds or conditions that are conservative in nature, thus erring on the side of caution. This practice leads to a large number of tickets that require no action (false positives). Elimination of false positive alerts is imperative for effective delivery of IT Services. It is also critical for the subsequent problem determination and resolution. All operational data, including events and problem records, will be used for automated resolution recommendation.

Notes

Acknowledgements

Automating resolution of complex events is exciting but unknown territory for Service provides. We are grateful to the technical teams and executive leadership of IBM technical services for their trust and on-going support to our road to AI driven automation.

Also this work was done in collaboration with Florida International University and St. Johns University, and we thank our collaborators: prof. Dr. Tao Li (deceased), prof. Dr. G. Ya. Grabarnik, Dr. Liang Tang, Dr. C. Zeng, Dr. Wubai Zhou, and Qing Wang.

References

  1. 1.
    Alpaydin E (2014) Introduction to machine learning. MIT Press, CambridgezbMATHGoogle Scholar
  2. 2.
    Bird S (2006) NLTK: the natural language toolkit. In: Proceedings of the COLING/ACL on Interactive presentation sessions. Association for Computational Linguistics, pp 69–72Google Scholar
  3. 3.
    Srikant R, Agrawal R (1996) Mining quantitative association rules in large relational tables. In: Proceedings of ACM SIGMOD, pp 1–12Google Scholar
  4. 4.
    Yin X, Han J (2003) CPAR: classification based on predictive association rules. In: Proceedings of SDMGoogle Scholar
  5. 5.
    Pazzani MJ, Merz CJ, Murphy PM, Ali K, Hume T, Brunk C (July 1994) Reducing misclassification costs. In: Proceedings of ICML, New Brunswick, NJ, pp 217–225CrossRefGoogle Scholar
  6. 6.
    Li J (2006) Robust rule-based prediction. IEEE Trans Knowl Data Eng (TKDE) 18(8):1043–1054CrossRefGoogle Scholar
  7. 7.
    Chang S, Zhou J, Chubak P, Hu J, Huang TS (2015) A space alignment method for cold-start tv show recommendations. In: Proceedings of the 24th International Conference on Artificial Intelligence, pp 3373–3379Google Scholar
  8. 8.
    Li L, Chu W, Langford J, Schapire RE (2010) A contextual-bandit approach to personalized news article recommendation. In: WWW. ACM, pp 661–670Google Scholar
  9. 9.
    Schein AI, Popescul A, Ungar LH, Pennock DM (2002) Methods and metrics for cold-start recommendations. In: SIGIR. ACM, pp 253–260Google Scholar
  10. 10.
    Petrov S, Das D, McDonald R (2011) A universal part-of-speech tagset. arXiv preprint arXiv:1104.2086.Google Scholar
  11. 11.
    Potharaju R, Jain N, Nita-Rotaru C (2013) Juggling the Jigsaw: towards automated problem inference from network trouble tickets. In: NSDI, pp 127–141Google Scholar
  12. 12.
    Zeng C, Wang Q, Mokhtari S, Li T (2016) Online context-aware recommendation with time varying multi- armed bandit. In: SIGKDD, pp 2025–2034Google Scholar
  13. 13.
    Zhou W, Tang L, Zeng C, Li T, Shwartz L, Ya Grabarnik G (2016) Resolution recommendation for event tickets in service management. IEEE Trans Netw Service Manag 13(4):954–967CrossRefGoogle Scholar
  14. 14.
    Castillo LA, Mahaffey PD, Bascle JP (2008) Apparatus and method for monitoring objects in a network and automatically validating events relating to the objects. U.S. Patent, US 7,469,287 B1.Google Scholar

Copyright information

© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Kristof Kloeckner
    • 1
  • John Davis
    • 2
  • Nicholas C. Fuller
    • 3
  • Giovanni Lanfranchi
    • 1
  • Stefan Pappe
    • 4
  • Amit Paradkar
    • 3
  • Larisa Shwartz
    • 3
  • Maheswaran Surendra
    • 5
  • Dorothea Wiesmann
    • 6
  1. 1.Global Technology ServicesIBM (United States)ArmonkUSA
  2. 2.Global Technology ServicesIBM (United Kingdom)HursleyUK
  3. 3.IBM Research DivisionIBM (United States)Yorktown HeightsUSA
  4. 4.Global Technology ServicesIBM (Germany)MannheimGermany
  5. 5.Global Technology ServicesIBM (United States)Yorktown HeightsUSA
  6. 6.IBM Research DivisionRüschlikonSwitzerland

Personalised recommendations