For DevOps engineers, “noise” is the enemy of productivity. In this context, the noise we’re talking about is unnecessary or low-priority alerts and notifications that distract engineers from identifying serious issues—and ultimately can cause alert fatigue syndrome, in which alerting systems are ignored altogether.
Without the application of a well-constructed noise reduction plan, alert noise will end up taking away from the focus of solving the actual problem that may be causing a more critical issue.
So, how can you fight alert noise? You might be tempted to assume that the answer lies in reducing the number of alerting tools you use, but that’s probably unrealistic. Teams today are dealing with complex application stacks and multiple tools and data sources, all of which can produce their own alerts for issues that may be interdependent or isolated. You simply can’t take tools out of your stack.
What you can do, however, is analyze and manage alerts more effectively. Dealing with alert noise ultimately boils down to developing an effective problem analysis strategy. Let’s explore how, with a specific focus on how AIOps factors into keeping alerts manageable.
Reduce the Time in Analyzing Alerts
While reducing noise is important, DevOps teams risk losing valuable time solving critical problems if they also have to create and/or add systems to properly analyze alerts and their sources and priority level. Maintaining this system also would be a source of unnecessary labor that may reduce the alert noise, but also reduces the amount of time that DevOps staff can work on the solution to the root cause— which brings up another consideration. When coming up with a plan, the amount of complexity involved in adding the monitoring tools necessary to cover an entire application and/or service can be equally laborious compared to not having a solution at all. At this point, we may be merely shifting the labor emphasis.
This is Where AIOps Comes In
Now, let us envision a way that a team can gain full insight into a root cause without exposure to alert noise. The team doesn’t need to analyze every single alert, which would include:
- Duplicate alerts.
- Alerts from multiple sources (tools, microservices).
- Informational alerts (warnings).
Instead, the team can let AIOps analyze and classify the stream of alerts and identify critical issues. DevOps no longer would need to spend valuable collaboration time analyzing every link in the toolchain. When a team has to analyze every tool, the team could produce false positives, redirect resources toward the wrong solution, spend too much time trying to identify the root cause and create excessive alert noise. Let us envision a way in which the team does not need to spend excessive time in analysis and can experience more effective collaboration in service of solving the actual problem.
AI and machine learning can relieve this manual process through AIOps. Automated collection of data across the application stack, alert correlation, identifying root cause and decision-making can be leveraged by a team to get to the problem immediately without all of the messy work of manual analysis, sorting and identifying root cause. This level of automation allows for needed human interaction through collaboration across teams, which can result in faster resolution, decreased downtime and effective communication of key events to company staff and end users.
Analyzing to Predict Future Problems
Another benefit of AIOps and the power of automated data collection, analysis, issue identification and decision-making is the ability to predict future events. A DevOps team can leverage these insights into effective preventative measures. The ability of AIOps to produce these insights will help a team find ways to automate and/or improve operations. Reducing alert noise no longer needs to be a goal because it is happening through a smooth interaction between the human team and the AI.
There is a growing need for DevOps teams to utilize the power of AIOps. While human teams are not replaceable, redundant and time-consuming tasks are. The ability of AIOps to effectively traverse a complex toolchain with isolated issues to analyze alert importance, correlate alerts, collect and analyze relevant data, identify root cause and produce holistic actionable insights eliminates inefficiencies in problem-solving and reduces alert noise.
Making DevOps less noisy is a goal with its roots in effective use of information. Alerts serve to inform, but thanks to the increasing complexity of applications, alerts become noise. Leveraging the power of AIOps serves to do a lot of the dirty work for a team so that noise can be reduced—but more importantly, it can make operations run smooth and keep customers happy.
