In 2009, among developers at a conference in Belgium hosted by project manager and Agile practitioner Patrick Debois, the term DevOps was born. Suddenly, DevOps took the tech and business scene by storm. Today, the DevOps practice is integral to numerous businesses across the world.
From planning through continuous delivery, collaboration and automation, the marriage of development and operations is successful. With DevOps, automation has increased, testing has become easier, and deployments go out at a faster rate.
However, there are still elements that could slow down the DevOps process and undercut its automation and continuity, including such tasks as taking care of system alerts and managing and defining rules and filters — which is why AIOps has emerged as the next frontier in DevOps. AIOps makes it possible to unlock the full power of DevOps.
What is AIOps?
Algorithmic IT Operations (AIOps) is a solution-based term that describes the use of machine learning and artificial intelligence to automate tasks and processes that have traditionally required the involvement of a human employee. AIOps uses algorithms where known, monotonous and everyday mundane problems can be resolved with AI, while human engineers solve new and more complex problems. This whitepaper discusses the best use cases for AIOps on a root-cause analysis basis, and what benefits and solutions AIOps provides.
Any experienced system administrator or DevOps or site reliability engineer has stories about the entire network slowing down (for unknown reasons), or receiving a monitoring alert at 3:00 AM saying that multiple servers have crashed. And they can tell you the headaches they encountered trying to find the reason why.
Of course, the first thing to do is look at the logs, but logs only tell half of the story. What about the other half — that doesn’t predict when these issues could occur again? System outages are common in the tech world, whether a brand-new startup in someone’s garage or the latest outage at YouTube, no one is completely safe.
When IT teams are facing a system outage, the first thing they must do is to identify the root cause. In this case, AIOps collects metrics, events, incidents, traces, and every other piece of data that it needs. One could say that AIOps automates the discovery of normal, critical and non-critical behavioral patterns. From there, the user understands what’s causing the biggest issues at hand and how it can be taken care of.
Why AIOps Matters
After the data is collected, it is presented to the user in a visual format for the infrastructure and dependencies that are used to power the service. The individual working on the task can quickly identify the problem and start the investigation. From the beginning of that investigation, the user has access to all relevant information from across the monitoring ecosystem as well as the change plans and the actual change that may have caused the issue. Once the team has identified the root cause of an incident, they can start to automate the remediation tasks for that issue and initiate the incident process, gaining approval as needed, and continually communicating with all stakeholders through to resolution.
Then comes prevention of future outages and slowdowns — which is put into place by connecting the services of the business to the infrastructure. This will give the user and the company better visibility and understanding of the components that are making the business run as a service. Therefore, it is vital for the IT team to have a thorough understanding of their environment, while eliminating the distance between technology silos, and giving everyone a clearer picture of each business service.
Next is keeping all of the services up-to-date. AIOps will run discovery jobs on a daily and nightly basis, ensuring the accuracy of the maps. Plus, infrastructure is always changing, and new technologies are continually being discovered while other components become obsolete. AIOps can play a critical role by automatically keeping your services up-to-date.
Once business services are mapped out, system alerts from all monitoring systems need to be set. An AIOps practice could take in the monitoring errors coming from your monitoring tools and reduce the number of alerts via machine learning algorithms you’ve created — which will help to eliminate false positive alerts and lets the team focus on silos that are important for a particular system outage. Once the incident has been identified and troubleshooting begins, the next step is to prioritize the issues and automate the fix via orchestration automation.
Monitoring of application usage and cost
When your monitoring setup is gathering information about CPU usage and irregular metric activity on your system, AIOps will monitor regular metric activity. If the range exceeds the normal use rate of the system, that anomaly will trigger the creation of an alert automatically. It will also create an incident report so the user would be able to track it from an IT service management perspective. Later, the user would be able to view their management dashboard to view all services provided by the AIOps platform. From there, the user can quickly identify service issues. A good AIOps tool will present the user with a detailed layout of the issues and categorize each issue by its severity.
Prioritization of important tasks
When the goal is quick resolution, the most important task should be zeroing in on the root cause of the biggest issue plaguing your system. Once that has been determined, then you can progress to monitoring your data. Only after considerable monitoring should AI be approached on a step-by-step basis.
Start with applying an AIOps structure that gives you an efficient groundwork in gathering robust amounts of data that make it easy to take action and monitor proficiencies that disclose patterns.
Next, research the point to which those patterns let you predict occurrences. Make sure you have a hands-on IT team that allows you to decrease not only your mean time to repair but also the number of incidents you face.
The AIOps methodology is growing daily, and its implementation is becoming more and more crucial. AIOps can save valuable time and effort in root-cause analysis. Work with machine learning-powered root-cause analysis to achieve an extrapolative state where you can control an incident and its impact before it even affects your main business services and the customer experience.