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Leveraging AI in Service Automation Modeling: From Classical AI Through Deep Learning to Combination Models

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Service-Oriented Computing (ICSOC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11895))

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Abstract

With the advent of cloud, new generations of digital services are being conceived to respond to the ever-growing demands and expectations of the market place. In parallel, automations are becoming an essential enabler for successful management of these services. With such importance being placed on digital services, automated management of these services – in particular, automated incident resolution – becomes a key issue for both the provider and the users. The challenge facing automation providers lies in variability and the frequently changing nature of the monitoring tickets that provide the primary input to automation. Despite the value of the correct automation at the correct time, it is also important to remember that triggering an incorrect automation may damage the smooth operation of the business. In this paper, we discuss AI modeling for automation recommendations. We describe a wide range of experiments which allowed us to conclude an optimal method with respect to accuracy and speed acceptable to service providers.

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Author information

Authors and Affiliations

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

    Qing Wang, Larisa Shwartz & Jinho Hwang

  2. Department of Math and Computer Science, St. John’s University, Queens, NY, 11439, USA

    Genady Ya. Grabarnik

  3. IBM Research–Zurich, 8803, Rueschlikon, Switzerland

    Michael Nidd

Authors
  1. Qing Wang
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  2. Larisa Shwartz
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  3. Genady Ya. Grabarnik
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  4. Michael Nidd
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  5. Jinho Hwang
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Corresponding author

Correspondence to Qing Wang .

Editor information

Editors and Affiliations

  1. Laboratory for Analysis and Architecture, Toulouse, France

    Sami Yangui

  2. National Engineering School of Sfax, Sfax, Tunisia

    Ismael Bouassida Rodriguez

  3. Laboratory for Analysis and Architecture, Toulouse, France

    Khalil Drira

  4. RMIT University, Melbourne, VIC, Australia

    Zahir Tari

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Wang, Q., Shwartz, L., Grabarnik, G.Y., Nidd, M., Hwang, J. (2019). Leveraging AI in Service Automation Modeling: From Classical AI Through Deep Learning to Combination Models. In: Yangui, S., Bouassida Rodriguez, I., Drira, K., Tari, Z. (eds) Service-Oriented Computing. ICSOC 2019. Lecture Notes in Computer Science(), vol 11895. Springer, Cham. https://doi.org/10.1007/978-3-030-33702-5_14

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  • DOI: https://doi.org/10.1007/978-3-030-33702-5_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33701-8

  • Online ISBN: 978-3-030-33702-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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