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.
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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.
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Kloeckner, K. et al. (2018). Gaining Insight from Operational Data for Automated Responses. In: Transforming the IT Services Lifecycle with AI Technologies . SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-94048-9_2
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DOI: https://doi.org/10.1007/978-3-319-94048-9_2
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