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IT Operations have seen huge changes in the past two decades, but none may be more important than the adoption of artificial intelligence (AI) and machine learning (ML) to speed, enhance, and automate monitoring and management of IT infrastructures. Since 2017, AIOps tools have leveraged big data and ML in day-to-day operations and promise to become an important tool for IT organizations of every size.
But what even is AIOps? Let’s take a look at the basics of the technology, explore what it was designed to do, and see how it is developing.
What is AIOps?
By leveraging big data and ML in traditional analytics tools, AIOps is able to automate some parts of IT operations and streamline other elements through insights gained from data. The aim is to reduce the time burden placed on IT ops teams by administrative and repetitive activities that are still vital to the operation of the larger enterprise.
AI-enabled Ops solutions are able to learn from the data that organizations produce about their day-to-day operations and transactions. In some cases, the tools can diagnose and correct issues using pre-programmed routines, such as restarting a server or blocking an IP address that seems to be attacking one of your servers. This approach provides a few advantages:
- It removes humans from many processes, only alerting when intervention is required. This means fewer operational personnel and lower costs.
- It integrates AIOps with other enterprise tools, such as DevOps or governance and security operations.
- It can detect trends and be proactive. For example, an AIOps tool can monitor an increase in errors logged by a switch and predict that it is about to fail.
AIOps Categorization
AIOps is really an existing category of tools known as CloudOps and Ops tools, repurposed with AI subsystems. This is leading to a number of new capabilities, such as:
- Predictive failure detection: This is achieved by using ML to analyze the patterns of activity of similar servers and determine what has resulted in a failure in the past.
- Self-Healing: Upon spotting an issue with the cloud-based or on-premises component, the tool can take pre-preprogrammed corrective action, such as restarting a server or disconnecting from a bad network device. This should address 80 percent of ops tasks, now automated for all but the most critical issues.
- Connecting to remote components: The ability to connect into remote components, such as servers and networking devices both inside and outside of public clouds, is critical to an AIOps tool being effective.
- Customized views: Information dashboards and views should be configurable for specific roles and tasks to promote productivity.
- Engaging infrastructure concepts: This refers to the ability to gather operational data from storage, network, compute, data, applications, and security systems, and to both manage and repair them.
We can divide AIOps into four categories: Active, Passive, Homogeneous, and Heterogeneous:
Active
Active refers to tools that are able to self-heal system issues discovered by the AIOps system. This proactive automation, where detected issues are automatically remediated, is where the full value of AIOps exists. Active AIOps allows enterprises to hire fewer ops engineers while increasing uptime significantly.
Passive
Passive AIOps can look, but not touch. They lack the ability to take corrective action on issues they detect. However, many passive AIOps providers partner with third-party tool providers to enable autonomous action. This approach typically requires some DIY engagement from IT organizations to implement.
Passive AIOps tools are largely data-oriented and spend their time gathering information from as many data points as they can connect to. They also provide real-time and analytics-based data analysis to enable impressive dashboards for operational professions.
Homogeneous
These AIOps tools live on a single platform, for example employing AI resources native to a single cloud provider like Amazon AWS or Microsoft Azure. While the tool can manage services such as storage, data, and compute, it can only do so on that one provider’s platform. This can impair effective operational management for those servicing a hybrid or multi-cloud deployment.
Heterogeneous
Most AIOps tools are heterogeneous, meaning that they are able to monitor and manage a variety of different cloud brands, as well as native systems operating within the cloud providers. Moreover, these AIOps tools can manage traditional on-premises systems and even mainframes, as well as IoT and edge-based computing environments.
Conclusion
AIOps creates opportunities for efficiency and automation that will reduce costs for businesses and free up time for IT Operations to invest elsewhere, in more valuable activities. As the field evolves, so too will the tools, innovating and developing new abilities and consolidating existing capabilities into core services.