Using ML/AI to Support Infrastructure Monitoring

Error infrastructure monitoring

Content By Devops .com

Successful infrastructure monitoring enables IT teams to ensure constant uptime and performance of their company’s systems. Technologies like machine learning (ML) and artificial intelligence (AI) benefit infrastructure monitoring by more quickly collecting and analyzing data from all of the hardware and software components that comprise the IT stack. Infrastructure changes are occurring faster than ever before, but complex systems, the unique nature of applications and lack of IT skillsets can cause challenges when integrating with these newer technologies. However, it’s more important than ever that sysadmins and DevOps teams understand how ML and AI can mitigate these roadblocks, support them in staying on top of infrastructure performance and rapidly address issues that arise.

Intelligent Monitoring Support for Complex Systems

The most tangible result of intelligent infrastructure monitoring tools and processes is near-immediate alerting of performance and uptime issues, which can then be addressed in an efficient and effective manner so no business interruptions occur. However, complex systems can stunt these benefits if ML and AI are not being used and manual monitoring protocols are still in place.

Tools that use ML or AI lessen the work of IT staff immensely, freeing up critical business resources and aiding in overall productivity. Both technologies can automatically identify and update all IT stacks that comprise an enterprise’s infrastructure to keep systems up-to-date and aligned with established key performance indicators (KPIs). In addition, intelligent offerings can detect and factor those metrics against set standards so that early alerts to an “unhealthy” section of infrastructure can be identified, even as the IT stack is constantly changing. This drastically speeds up troubleshooting efforts.

Differentiation in Applications

The different applications supported by the various IT stacks will most often have unique service-level agreements (SLAs) for their performance and uptime, as well as remedies or penalties should those service levels not be achieved. Plus, system loads that stress the underlying infrastructure are frequently changed. For these reasons, it is important to identify what constitutes a “healthy” IT stack so that these minute parts of the infrastructure are not overlooked due to the variation involved.

ML and AI can be programmed to track system baselines that support a “healthy” IT stack. These technologies are particularly great at finding novel and unusual patterns in data. As the monitoring and observability landscape becomes more complex over time, driven by real changes in how developers build applications and systems, the ability to spot and detect such patterns in data can be crucial in helping make sense of it, further cutting down efforts on manual searching, detective work and “death by dashboards,” which we’ve all experienced at one time or another.

Supporting IT Team Skills with Intelligence Technology

The role of sysadmins—and to a greater extent, developers—has shifted over the past few years to become nearly as complex as the infrastructure they oversee. Nowadays, it seems as though developers are required to have expertise in all aspects of infrastructure, from monitoring to Kubernetes to machine learning. This can take quite the toll on developers who possess such skills, but in a more realistic sense, developers that can do all these things are very hard to come by. The lack of these skillsets is pervasive in the industry, which is why ML and AI can be seen as supporting technologies—they can fill in these gaps, to an extent.

With built-in intelligence and automation, ML/AI can enable even the most inexperienced sysadmin or DevOps professional to monitor complicated infrastructure like a pro, taking on most of the time-intensive work around collecting and analyzing the data and identifying where to troubleshoot. The main goal is to put humans in the driver’s seat, utilizing ML and AI for granular discovery of system issues, providing the metrics or charts that might be most relevant to IT staff as they troubleshoot their system and reducing the cognitive load of developers.

With the vast benefits that intelligent technologies possess, integrating them into your IT stack can help mitigate challenges experienced with complex systems, application differentiation and the skills deficit experienced in the IT team. The important ingredient in making ML and AI effective in infrastructure monitoring is using tools that incorporate the right formulas, algorithms and automation that can best help determine success when it comes to your desired outcome.

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