AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges (2304.04661v1)

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.

• The paper presents a comprehensive review of AIOps in cloud platforms, analyzing methodologies from manual to fully automated operations.
• It identifies critical challenges such as data quality, scarce public datasets, and non-stationary metrics that impact service reliability.
• The study highlights opportunities for enhancing IT operations through AI-driven incident detection, failure prediction, and automated remediation.

AI for IT Operations (AIOps) in Cloud Platforms

Introduction to AIOps

Artificial Intelligence for IT operations, or AIOps, integrates AI technology with IT operations. Its objective is to offer actionable insights that help maximize system availability and efficiency, particularly within cloud infrastructures. AIOps addresses a broad spectrum of problems in IT operations and utilizes AI capabilities in various use cases such as incident detection, failure prediction, root cause analysis, and automated actions.

Challenges in Ensuring Service Reliability

In cloud services, guaranteeing reliability and meeting Service Level Agreements (SLAs) is crucial. Unexpected service downtimes can have severe financial repercussions and damage customer trust. Disruptions need to be predicted and rectified swiftly to maintain high availability. This need gives rise to the adoption of AIOps technologies in enterprises, which are evolving towards automating IT Operations.

Key Components of AIOps

AIOps solutions are not straightforward to implement and involve complex processes that are broken down into different maturity levels:

1. Manual Operations: Basic processes without AI or ML models.
2. Human-centric AIOps: Operations are mainly manual with AI models assisting sub-procedures.
3. Machine-centric AIOps: Major components of operations are empowered by AI, requiring minimal human intervention.
4. Fully-automated AIOps: Entire operational processes are automated, pursuing continuous integration, deployment, monitoring, and correction pipelines (CI/CD/CM/CC).

AI techniques play a significant role in solving intertwined problems in AIOps. To evaluate their effectiveness, a comprehensive survey on AIOps is necessary. This survey will help the community better understand AIOps and accelerate its capabilities.

Data Handling in AIOps

Data plays an essential role in AIOps. Diverse telemetry data such as metrics, logs, traces, and more are required. Unfortunately, public datasets that are essential for research progress are scarce. Metrics are numerical and provide snapshots of system behavior, including collective statistics. Logs, on the other hand, capture detailed runtime information, although they pose challenges in their voluminous and complex nature. Traces provide an overview of system events and application flows, beneficial in root cause analysis due to the encapsulated topological information.

Importance of AIOps Research

Research studies on AIOps cover automated log analysis, time-series anomaly detection, RCA with multimodal data, failure prediction models, and optimizing automated actions for efficient operations. However, practical AIOps research is still in an early stage, primarily focusing on detection and analysis. The prospect of AIOps propels us towards fully automated operations, but common AI challenges such as data quality, label scarcity, non-stationarity, and a lack of public benchmarking must be overcome.

Final Thoughts

AIOps stands at the intersection of AI advancements and the burgeoning need for sophisticated IT operations management. As digital ecosystems steer towards a cloud-first approach, AIOps becomes invaluable for sustainable growth and reliability. The survey underlines the immense potential AIOps holds and the pivoting role of AI in navigating complex IT infrastructures towards a more automated, efficient future.