For nearly a decade we have witnessed a rapid evolution of cloud infrastructures, applications, and operations. At first, the cloud was mainly about migrating legacy applications from on-premise infrastructures to cloud data centers. This was a key to taking advantage of the scalability, flexibility, and Quality of Service (QoS) of cloud infrastructures. In later stages, application developers had to revise the design and implementation of their applications to allow them to benefit from their direct deployment on the cloud. Over the years, application developers and cloud providers realized the benefits of unifying application development with cloud operations, which gave rise to the DevOps (Development and Operations) paradigm. The latter complements the agility of software development processes with flexible the (re)configuration of the cloud operations. In the scope of DevOps, application development and cloud operations are closely related and jointly optimized.
Nowadays, enterprises leverage large scale cloud computing infrastructures, which comprise elements from public cloud, private cloud, and on-premise data centre infrastructures. Furthermore, in modern deployments, the complexity of the underlying cloud infrastructures is completely virtualized thanks to container technologies and microservices. Specifically, most non-trivial cloud applications are comprised of a rich set of microservices, which can be deployed and scaled-up independently in different parts of the infrastructure. In this context, application developers and cloud infrastructure operators are provided with large volumes of data about the operation of their underlying cloud infrastructures and of their applications. In the years to come, the processing of these data will provide valuable insights regarding the operation of the cloud infrastructure. This will substantially increase the automation and intelligence of DevOps operations.
The processing of large amounts of data from the cloud infrastructure enables enterprises to realize a shift from DevOps towards data-driven models for configuring and managing cloud infrastructures. One of the most prominent models for data-driven infrastructure management is AIOps, which is expected to help enterprises revolutionize their IT operations based on Artificial Intelligence (AI) technologies. AIOps is about using AI to process data from the infrastructure towards automating and optimizing cloud operations. As a prominent example, AIOps enables automated, AI-based detection of incidents, management of faults, and intelligent root cause analysis.
AIOps deployments assess, detect, analyze, and resolve incidents across mission-critical workloads over virtualized cloud infrastructures. The main components of an AIOps infrastructure include:
AIOps provide a host of benefits to cloud operators and application developers. Specifically:
AIOps functionalities are usually implemented as AI/ML pipelines over a vast amount of data collected from the virtualized cloud infrastructure. A typical AIOps pipelines consist of the following processing stages:
In the next couple of years AIOps will be increasingly deployed to enhance the automation and intelligence of cloud operations. In the medium term, AIOps platforms will offer a rich set of monitoring and analytics functionalities, such as behavioral analysis, failure pattern matching, and predictive analytics. Enterprises must therefore consider the state of the art in AIOps platforms towards positioning themselves in the data driven infrastructure management landscape and planning their adoption steps accordingly.
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