This paper aims to describe the concept of applying Artificial Intelligence to IT Operations (AIOps) and its main components, Big Data, Machine Learning and Trend Analysis. The concept was implemented by developing a multi-layered fusion of the technologies that powers the components in AIOps platforms present on the IT market. The core of an AIOps platform is represented by the Big Data organization structure and by a massive parallel data processing platform like Apache Hadoop. The ML component of the platform is able to infer the future behaviour and the regular operations that are performed from the large volume of collected data, in order to develop the ability to automate the activities. AIOps platforms find their place especially in very complex IT infrastructures, ones that require constant monitoring and quick decisions in case of failures. The case study is based on the Moogsoft AIOps platform, and its features are presented in detail, using the Cloud trial version, clearly showing the potential of such an advanced tool for infrastructure monitoring and reporting. The experiment was focused on the way Moogsoft is monitoring computing resources, is handling events and records alerts for the defined timespan, alerts grouped by category (like web services, social media, networking). The platform is also able to display at any given moment the unresolved situations and their type of origin, and includes automated remediation tools. The study presents the features of this software category, consisting in benefits for the business environment and their integration into the Internet-of-Things model.
Keywords: Big Data, Machine Learning, AIOps, business performance.
Empowering IT Operations through Artificial Intelligence – A New Business Perspective
