Cloud-Native Architecture for Distributed Systems that Facilitates Integration with AIOps Platforms

DevOps has significantly enhanced application operations through the utilization of containers and CI/CD. It still relies on human intervention in the event of failures in any system component. Many existing solutions are limited to specific issues, such as reacting to server outages and scaling them up. As the complexity of distributed systems continues to grow due to the simultaneous operation of numerous components, even minor unavailability can substantially impact application reliability and result in significant economic consequences for businesses. Therefore, it is imperative that the solutions being developed minimize risks and increasingly automate these operations. In light of these challenges, the emergence of AIOps offers a promising solution using artificial intelligence techniques, including machine learning and big data, to operate and maintain application infrastructures, reduce operational complexity, and automate IT operations processes. Implementing such solutions has been shown to improve system quality and significantly reduce the time it takes to detect errors and recover from them. These advancements mark significant progress in the realm of operations. However, despite these benefits, widespread adoption of AIOps solutions by most companies remains limited due to the challenges associated with implementing them in large projects and the lack of clear integration pathways for emerging solutions. In this paper, we propose a holistic architecture that facilitates the integration of cloud-native distributed systems with these new solutions.