How the AIOps Architecture Increases IT Operations Productivity
IT operations teams face the challenge of maintaining service uptime and delivering prompt remediation – all while addressing the growing needs of the business by continuing to roll out and support new services and features.
All this, of course, is happening in an era of increased complexity. Both legacy and new technologies coexist, often uncomfortably, under the watchful eyes of the IT ops team.
Enter artificial intelligence for IT operations (AIOps). The AIOps architecture brings together machine learning and big data technologies to turbocharge the operational and automated aspects of managing today’s IT infrastructure. Like no other technology that came before it, AIOps is improving the operational efficiency of the delivery of business services.
As a result, the market is taking off. Gartner predicts that exclusive use of AIOps and digital experience monitoring tools by large enterprises to monitor applications and infrastructure will rise from 5% in 2018 to 30% in 2023.
The AIOps architecture is increasingly proving to be a critical new element for addressing challenges resulting from expanding networks and increased complexity. Today it’s earning accolades for increasing the productivity of the IT operations organization.
Deriving AIOps Architecture Insights
The basic manner in which AIOps does this is two-fold:
- It provides a level of actionable intelligence that was previously not available. It does this by providing insights driven by leveraging machine learning and big data technologies.
- The second factor is its ability to act upon these insights in a rapid, effective and efficient manner using automation.
Let’s take a look at the architectural elements of AIOps that bring about these gains by focusing on these two primary aspects. Machine learning and big data are advanced technologies that depend on the availability of massive data sets and analytic engines in order to provide insights.
All this starts with the infrastructure monitoring ecosystem that the AIOps architecture must provide. It is essential that the monitoring ecosystem encompasses the entire spectrum of infrastructure elements. After all, gaps in visibility directly affect the ability to monitor and manage services.
The AIOps architecture delivers the ability to work with multiple monitoring tools, increasing its effectiveness. The architecture facilitates mechanisms to deal with the immense volume and wide disparity of the many kinds of data that is generated by the infrastructure. Common sources of useful data include the output from:
- IT infrastructure monitoring
- Application performance monitoring
- Digital experience monitoring
- Network performance monitoring and diagnostics
Acting Upon Insights
But in order for AIOps to derive insights, this data must be normalized, enriched and stored somewhere. AIOps architectures typically provide a data lake or a similar repository to facilitate the collection, manipulation and dispersion of the data.
It is only after this ability to ingest, enrich and store data, that machine learning and big data techniques can be applied to glean actionable intelligence. Various kinds of processing including advanced analytics, pattern matching, natural language processing, correlation and anomaly detection can then be applied to generate usable information.
Having actionable intelligence is the first step. But to bring about productivity, it is necessary to act upon this intelligence. AIOps architectures provide for and make extensive use of automation technologies to achieve this goal. There are many kinds of automation that AIOps systems use, and they play a variety of roles when it comes to IT operations management (ITOM).
Some typical examples of AIOps and ITOM include:
The Optanix Platform provides dynamic baselining to determine what constitutes normal behavior of an application or service. In addition, it generates dynamic thresholds and tracks mean time to threshold. This provides a predictive approach to ITOM and a superior understanding of the entire service delivery infrastructure.
- IT workflow automation, which provides for the efficient execution of predetermined workflows. These workflows can be kicked off automatically or on demand to perform a vast variety of activities including issue detection, remediation and multiple aspects of classic event, incident and problem management.
- Orchestration systems, which provide more sophisticated automation capabilities and can be used to provision entire service delivery streams. AIOps architectures that support software-defined Networks (SDN) and network function virtualization (NFV) depend extensively on orchestration to deliver business value.
AIOps architectures provide the means to rapidly develop and use automation. These include the use of low-code process automation capabilities that make it easier and faster to generate workflows and automation libraries that store and provide for the deployment of workflows across the IT infrastructure.
The architecture of AIOps platforms supports the capabilities that provide the means to make IT operations more productive. They do this by providing actionable intelligence and the ability to respond to this intelligence in an effective, efficient and speedy manner.
To learn more about what AIOps is about and what is driving it’s increasing adoption, please refer to our recent post “What is AIOps & What’s Driving the AIOps Revolution?”