AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges

TL;DR

This paper reviews the application of AI techniques in cloud-based IT operations, highlighting current trends, challenges, opportunities, and categorizing key tasks like incident detection and failure prediction.

Contribution

It provides a comprehensive taxonomy of AI methods for AIOps tasks, discusses data challenges, and identifies underexplored areas with potential for future AI advancements.

Findings

AIOps tasks include incident detection, failure prediction, root cause analysis, and automation.

Analysis of IT operational data reveals scale and complexity challenges.

Identification of underexplored topics that could benefit from AI research.

Abstract

Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes, particularly in cloud infrastructures, to provide actionable insights with the primary goal of maximizing availability. There are a wide variety of problems to address, and multiple use-cases, where AI capabilities can be leveraged to enhance operational efficiency. Here we provide a review of the AIOps vision, trends challenges and opportunities, specifically focusing on the underlying AI techniques. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions. We discuss the problem formulation for each task, and then present a taxonomy of techniques to solve these problems. We also identify relatively under explored topics, especially those that could significantly benefit from advances in AI literature. We also provide insights into the trends in this field, and what are the key investment opportunities.