AI for Information Technology Operation (AIOps): A review of IT Incident Risk Prediction

Salman Ahmed, Muskaan Singh, Brendan Doherty, Effirul Ramlan, Kathryn Harkin, Damien Coyle

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The advancement of Artificial Intelligence has led to a surge in its application in Information Technology (IT) Operations, often termed Artificial Intelligence for IT Operations (AIOPS). One of the most challenging problems in AIOPS is IT Service Management (ITSM), which deals with incidents and anomalies of users, often referred to as tickets. These tickets are resolved by the IT firm support system, which plays a significant role in the company's user experiences, productivity, and profit. Recent advances have been made to automate the prediction of IT incidents and resolve them in a minimal time, utilizing AI models. In this paper, we take stock of the work in this domain and review the challenges. We also highlight the open topics that require further investigation for the advancement of the field.
Original languageEnglish
Title of host publicationInternational Conference on Soft Computing & Machine Intelligence
PublisherIEEE
Publication statusAccepted/In press - 25 Jul 2022

Bibliographical note

We are grateful for access to the Tier 2 High-Performance Computing resources provided by the Northern Ireland High-Performance Computing (NI-HPC) facility funded by the UK Engineering and Physical Sciences Research Council (EPSRC), Grant Nos. EP/T022175/ and EP/W03204X/1. Damien Coyle is supported by the UKRI Turing AI Fellowship 2021-2025 funded by the EPSRC (grant number EP/V025724/1). Salman Ahmed is supported by a Dr. George Moore Ph.D. scholarship.

Keywords

  • Artificial Intelligence for IT Operations (AIOPS)
  • IT Incidents
  • Risk prediction
  • Dataset Imbalance
  • IT Service Management (ITSM)
  • Information Technology Infrastructure Library (ITIL)

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