Paper Details

Name: Bhawna, Aarti Thakur
Country: India

Year: 2024
Volume: Volume-11, Issue-1 (January-June)
Page Number: 119-125

The term Algorithmic IT operations, or AIOps, was first used by Gartner. It stands for automated solutions made up of machine learning algorithms and methods to address unclear, difficult, and important operational IT challenges. Over the past few years, AIOPS has grown significantly, leading more organizations and providers to begin investigating AIOps solutions. AI operations were redefined as "Artificial Intelligence for IT Operations."
AIOps also lowers operating costs and human labour while increasing DevOps productivity and service quality. In this study, we first provide an overview of AIOps, including its elements, features, use cases, platform requirements, and practical difficulties. After that, we suggest a framework design for the AIOps platform based on the findings and research we have already done. In this paper, we present a new online log anomaly detection technique using DevOps that aids in greatly lowering the log anomaly detection time-to-value. AIOps automates operational tasks like artificial intelligence technologies, such as machine learning. It aids in the intelligent classification of log events, and their solution automation. This approach can automatically avoid potentially biased models generated by tainted log data and update the Log Anomaly Detection model continuously during runtime.
AIOps is still developing, necessitating ongoing education and development through empirical study and scientific research.
Keywords AIOps, Anomaly Detection, Artificial Intelligence, Devops, Log Operations, Machine Learning.