What is AIOps?

AIOps (Artificial Intelligence for IT Operations) is a term coined by Gartner in 2016 as an industry category for machine learning analytics technology that enhances IT operations analytics covering operational tasks include automation, performance monitoring and event correlations, among others.

Gartner themselves define [1] an AIOps Platform thus: “An AIOps platform combines big data and machine learning functionality to support all primary IT operations functions through the scalable ingestion and analysis of the ever-increasing volume, variety and velocity of data generated by IT. The platform enables the concurrent use of multiple data sources, data collection methods, and analytical and presentation technologies.”

Why is AIOps necessary?

Decades ago, IT operations used to be simple. There were few IT components involved – client, server, network, etc. and the environments used to be static. Manual analysis of such infrastructures was the way in which IT teams managed them. Over the years, there have been several significant changes in IT operations. All of the below factors are leading to the increased adoption of AIOps technologies.

  • Dynamic IT Environments: Technologies like containers and orchestration (e.g., Kubernetes) now make it possible for systems and applications to be elastic – i.e., be provisioned dynamically as demand increases and to be scaled down as demand reduces, with no manual intervention. Manual analysis of such environments is just not possible.
  • Complexity of IT Environment Monitoring: As IT technologies have become more automated, there have been many new components introduced in an IT architecture. Failure or malfunctioning of any of these components can affect IT services. Hence, every one of the components in an IT infrastructure need to be monitored. This dramatically increases the amount of data that has to be collected and monitored. IT operations teams regularly collect metrics from components as diverse as thermostats, power supplies, CPU, memory, application logs, user end points, mobile devices, virtual machines and a whole host of infrastructure services. Performance monitoring has well and truly become a big data problem. It is impossible to manually analyze and discern performance of every component in the service delivery chain.
  • IT is Vital for Businesses: As businesses have become more digital, they have become more dependent on IT. Failure of IT systems or slowness of these systems can affect business revenues. For example, consider a scenario where hundreds of workers from home are not able to login to a corporate network to do their jobs. This could cost the business hundreds of thousands of dollars in lost productivity. Hence, the need for IT operations to be proactive and efficient, so when a problem happens, it is resolved in the fastest possible manner.
  • Expert IT Staff Shortage: IT organizations are struggling to deal with shortage of expert IT staff. As a result, there is an expectation of IT operations to do more with less resources. This can only happen with more intelligent software that can automate and simplify IT operations tasks.

Benefits of AIOps

  • User Experience: Deliver higher uptime and better performance to the end users
  • Lower OPEX: Improved productivity of the support function & reduced reskilling/ upskilling pressure
  • Simplified Operations: Reduce clutter & surface only important actionable issues to reduce complexity of monitoring
  • Faster Turnaround: Augment support function with AI-driven assisted operations for faster resolutions

So, monitoring must evolve from just looking at hardware metrics to analyzing application codes and business transactions. The performance of an application should be measured with a user-centric view. This forms the basis of application performance monitoring (APM).

AIOps use cases

AIOps can support a wide range of IT operations processes. Here are the top use cases of AIOps:

  • Intelligent Alerting and Escalation: By observing event patterns and trends, AIOps tools can detect abnormal performance patterns and provide intelligent and proactive alerts to IT operations teams. This ensures that alert fatigue that IT operations teams often have to deal with. With AIOps tools, IT operations teams don’t have to deal with hundreds of spurious alerts but can focus only on real problems.
  • Event Correlation and Root Cause Analysis: IT applications use a number of software and hardware tiers and when a problem happens in one tier, this can ripple and affect all the other tiers as well. AIOps tools analyze data from all tiers, create causality/relationships, providing IT with correlated insights using which they can determine where the root-cause of a problem lies.
  • Anomaly Detection: By analyzing millions of performance metrics over time, AIOps tools identify anomalies in the infrastructure that if rectified can improve service performance and infrastructure utilization. Such proactive detection of performance issues can also avert service downtime and business impacting situations.
  • Automated Incident Management: Enterprises can not only set automatic alerts for incidents but also trigger automatic system responses to remediate the issues. By automating and resolving incidents in near real-time, enterprises can deliver a seamless user experience.
  • Capacity Planning and Management: Using AI-based, data-driven recommendations, workloads can be mapped to the right combination of servers and machines. With AI-enabled, real-time insights, IT teams can improve the capacity of IT infrastructure while reducing operational costs.

Components of a typical AIOps platform

The figure below shows a simplified view of the different components of an AIOps platform:

  • The data layer acts as the eyes of the AIOps platform, continuously observing the IT landscape. Metrics/data are obtained from different sources in the IT landscape.Data sources can include different monitoring tools, application logs, ITSM tools, etc.
  • The AI layer is the brain of the platform – it uses the data that is ingested and extracts actionable insights out of it. Different forms of machine learning including anomaly detection, prediction and correlation are some of the capabilities embedded in this layer.
  • The visualization layer offers the interface through which different stakeholders – business executives, DevOps teams, IT Ops teams, etc. interact with the platform.
  • The ultimate goal of AIOps is to simplify and automate IT operations. Based on the results of monitoring and analysis, intelligent automation can be used to automatically address anomalies/issues in the IT landscape, wherever possible. This is where IT automation comes in.

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