In the digital transformation era, application development takes a boundary-less approach. Applications are either built in the organization’s own datacenter or leverages IaaS, PaaS, FaaS or public cloud provider services.
As the IT landscape evolves, users expect the applications to be available at all times, across devices. For enterprises, the challenge in delivering to this expectation revolves around the following:
- How can we manage an application that is deployed across multiple environments and still ensure a seamless user experience?
- How do we pre-empt an issue before it affects the end user?
- How do we pinpoint whether the issue is in user’s device, application server, in the middleware or database, or the network that is being used to connect? Or worse, how can we tell if it is a piece of bad code that lies at the heart of the issue?
Often, these challenges stem as a result of support teams running operations with traditional monolithic monitoring tools, updating configuration management database (CMDB) manually, and using siloed approaches to datacenter, network, and application management. Support teams need dynamic and intelligent monitoring and management systems to deal with the current requirements.
This paper introduces you to the building blocks that an Operations Intelligence Platform needs to have in order to run AI-driven operations that effectively support a heterogeneous, multi-cloud, multi-geo platforms where applications are deployed.
AIOps: The next-frontier in IT operations
Today, application components are spontaneously created and destroyed to support dynamic load requirements, or to apply bug fixes/upgrades without impeding user access. At the same time, users access applications on a variety of devices such as mobile, smart watches, smart devices and so on. The monitoring and management components that support this fluid environment must be dynamic enough to understand the changes and provide real-time updates. This is critical to enable support teams to address issues proactively.
To enable such intelligent operations, organizations must re-think the way they conceptualize their requirement for support teams and empower them with a digital cockpit to manage the environment. Artificial Intelligence for IT operations (AIOps) will be key to managing multi-cloud and multi-vendor heterogeneous environments.
To execute AIOps, a comprehensive analytics and machine learning strategy that leverages holistic data is needed. Automation can then use the resulting intelligent insights to enable continuous improvements. Figure 1 illustrates key building blocks of AIOps.