The secret recipe for Observability

AIOps and Machine Learning
The secret recipe for Observability



By Thomas LaRock

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With traditional monitoring, data is aggregated and displayed. In case of problems, traditional monitoring generates warnings. Some are indispensable, but others distract from important information. Filtering out the unimportant warnings is therefore essential. Time for observability!

Observability is the new monitoring!
Observability is the new monitoring!

(Photo: © Alex – stock.adobe.com )

Complex IT infrastructures use microservice architectures and it is crucial that technology experts efficiently observe, monitor and analyze the cloud environments of their companies. You need alerts without the “noise” of unimportant information that leads to alert fatigue. Without noise, you manage to concentrate on the important signals.

This is easy to do with observability. Because it plays a crucial role when it comes to monitoring modern and complex IT systems, understanding their behavior and effectively improving the overall performance of these systems. It provides technical experts with a detailed insight into system performance, errors, vulnerabilities and failures. With this information, problems can be detected, monitored and resolved quickly. In short: Observability enables analyses in seconds that would otherwise take hours.

Integrated Intelligence

Traditional monitoring uses dashboards based on metrics and compares telemetry data with manual or statistically relevant thresholds. Typically, it focuses on a specific network, cloud, infrastructure, or application element so that technical experts can detect anomalies, investigate problems, and find solutions.

But monitoring has its limits. It does not provide cross-domain correlation, service delivery insights, and operational dependencies or predictability. In addition, silos are created during monitoring over time. This is where observability solutions come into play.

Information is crucial

Observability will not replace traditional monitoring. Instead, the information collected by the monitoring is used as decisive elements. Observability analyzes the collected data and compares it with the expected results and goals. With this data, technology experts can have a better overview of the state of their infrastructure and applications.

AIOps and ML make it possible to deliver observability solutions with predictive analysis capabilities, and thus go one step further. Observability platforms use it to detect potential problems before they occur and respond to them automatically and independently.

If technical experts need to intervene, you will be notified. The embedded AIOps and ML provide the necessary insights, automated analyses and practically actionable information about cross-domain data correlation. They also offer comprehensive real-time and historical metrics as well as trace data. So the signal will be clearly noticeable, and the final solution to the problem will be easier to find.

Proactively identify problems

This enables technology experts, including DevOps and security teams, to more proactively detect problems and anomalies. The teams can then automate tasks and design operational management, reporting and capacity planning in a cohesive and efficient manner across different IT areas. Thanks to AIOps and ML, observability solutions can:

  • strengthen business agility
  • Help professionals to identify problems and vulnerabilities
  • to characterize and predict effective changes to business services, components and activity states
  • create a lower administrative burden

Integrated observability solutions optimize IT efficiency, eliminate the need for redundant tools and thus help to reduce costs. It makes a big difference for IT teams when they can move from a reactive approach to a proactive one. With Observability, you can visualize and continuously analyze the relationships between business services and components, as well as deviations and dependencies. As a result, this also improves performance, compliance and resilience.

Observability takes traditional monitoring to a new level

Hybrid and remote work will continue to be part of everyday life in the future, just like SaaS applications and ubiquitous connected devices. AIOps and ML-supported observability solutions provide dynamic protection against disconnections that make communication impossible at the workplace and cause failures and interruptions in the production process.

However, one should not consider observability simply as another technology that one adds to the stack. Rather, it is an integrated solution for next-generation IT infrastructure, application and database performance management.

Thomas LaRock.
Thomas LaRock.

(Photo by SolarWinds)

Conclusion

Observability, including AIOps and ML, enables organizations of all sizes to more easily and holistically overview and manage IT service delivery. It provides cost savings through continuously improved performance and reliability. This improves the customer experience in complex, diverse and distributed hybrid and cloud-based environments. Observability and the integration of AIOps and ML take traditional monitoring practices to a whole new level.

About the author

Thomas LaRock is Head Geek at SolarWinds.

(ID:48393308)

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