AIOps: Analysing Cloud Failure Detection Approaches for Enhanced Operational Efficency

In recent years, Modern society is progressively moving in the direction of complex and distributed computing structures. Because of the high complexity of the organization, management teams execute regular checking and resolving processes to extend the consistency and reliability of modern system applications. Hence, the concept of automated and intelligent systems is receiving more attention from Information Technology (IT), manufacturing, and academic fields. In the modern day, IT organizations and institutions are growing faster to reduce human supervision because it is more problematic to handle. Therefore, in this research, Artificial Intelligence for IT Operations (AIOps) is analyzed to overcome modern IT management difficulties due to AI and Big Data. Even though, the research progress on AIOps is still unstructured and unknown, because of lacking principles in classifying impacts for data necessities, target goals, and factors. This review work provides an in-depth analysis to gather and establish several contributions to AIOps in a unique reference index. This research is focused to minimize cloud service interruption and ensure high system accessibility by utilizing AI methods. This comprehensive research supports researchers in accomplishing a better solution for cloud failure detection.