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Building Automated Data Driven Systems for IT Service Management

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Abstract

Enterprises and service providers are increasingly challenged with improving the quality of service delivery while containing the cost. However, it is often difficult to effectively manage the complex relationships among dynamic customer workloads, strict service level requirements, and efficient service management processes. In this paper, we present our progress on building autonomic systems for IT service management through a collection of automated data driven methodologies. This includes the design of feedback controllers for workload management, the use of simulation-optimization methodology for workforce management, and the development of machine learning models for event management. We demonstrate the applicability of the presented approaches using examples and data from a large IT service delivery environment.

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Acknowledgements

The authors would like to express their gratitude to Aliza Heching, David Northcutt, George Stark, and George Galambos, all employed by IBM, for helpful and constructive collaborations that helped us improve the quality of the model. In addition, we are indebted to Tao Li and Wubai Zhou (Florida Internation University), Genady Grabarnik (St. Johns University), and Chunqiu Zeng (Google) for their assistance in model development.

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  1. IBM Thomas J. Watson Research Center, Yorktown Heights, NY, 10598, USA

    Yixin Diao & Larisa Shwartz

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  1. Yixin Diao
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  2. Larisa Shwartz
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Correspondence to Yixin Diao.

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Diao, Y., Shwartz, L. Building Automated Data Driven Systems for IT Service Management. J Netw Syst Manage 25, 848–883 (2017). https://doi.org/10.1007/s10922-017-9430-3

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