How To Harness The Power Of Observability, AIOps And Automation

By: Ali Siddiqui, Head of AIOps Segment, Enterprise Software Division, Broadcom

Does one plus two ever equal 30? It can when you’re talking about combining the concept of observability with the power of automation and artificial intelligence for IT operations (AIOps). In this post, I’ll introduce the concept of observability and show how AIOps and automation can be powerful complements. You can use these approaches to deliver the capabilities IT teams need to track and optimize their dynamic modern environments.

Introduction To Observability: What It Means And Why It Matters

Today, many IT teams are struggling with an overload of metrics. In many organizations, massive amounts of metrics are being generated, the vast majority of which they may never look at. This leads to a case of severe metric fatigue. This reality, coupled with the increasing complexity and dynamism of today’s environments, is part of what’s driving a widespread move to observability.

At a high level, observability refers to the degree to which the internal state of a system can be inferred based on externally available outputs. Therefore, the more observable a system is, the more it will enable teams to understand, manage, and enhance its performance. Teams can use observability to gain new levels of visibility focusing on business services that drive digital transformation.

Observability is emerging as a critical consideration for today’s DevSecOps teams, who are tasked with adapting to the radical transformation of IT environments. In the past, monitoring systems were focused on capturing, storing, and presenting data generated by underlying IT systems. This meant that human operators were responsible for analyzing the resulting data sets and making necessary decisions.

This fundamental model doesn’t align with current realities, however. With the increasing prevalence of approaches like continuous integration/continuous delivery, DevOps, containers and microservices, environments continue to grow more dynamic, ephemeral, interrelated and complex. With basic monitoring techniques, teams may lack the visibility they need, and their manual processes can’t always scale to support the explosive growth in data volumes that arise in these modern environments.

Traditional monitoring approaches worked fine when an operator was tasked with tracking a simple, static system within an isolated computing stack. These systems typically had easily observable outputs that made it easy to understand and predict behavior. Today’s environments present a completely different picture. For example, a team may be responsible for a cloud-based microservices implementation that’s highly ephemeral, with elements in a virtually constant state of flux. In this type of environment, it’s difficult to apply traditional monitoring, virtually impossible to keep it consistently current, and it’s challenging to get the outputs you need to truly understand performance.

To address these limitations, teams can reorient their goals and approaches and move from monitoring to observability. It’s no longer about monitoring a monolithic computing stack or a discrete infrastructure element; it’s about making complex, modern ecosystems observable. By doing so, teams can fully capitalize on the agility of modern approaches while optimizing service levels at the same time.

Another critical consideration in this regard is the fact that monitoring can’t be an afterthought after development is done. Rather, observability should be a part of the software development life cycle, and it should be part of the culture, just like DevOps.

How To Capitalize On AIOps And Intelligent Automation

Through AIOps and intelligent automation, teams can establish a strong complement to their observability initiatives. IT teams should seek to address these core steps in AIOps initiatives:

• Acquire: They should harness various data sources from across the organization’s ecosystem.

• Aggregate: They should aggregate data from disparate sources and apply correlation to fully capitalize on the intelligence they capture.

• Analyze: Teams should focus on artificial intelligence and machine learning that filters through noise, gains more targeted insights, and identifies patterns that enable more accurate predictions.

• Act: They should find ways to automate root cause analysis and remediation, as well as the opening and closing of tickets.

Tips for Leveraging AIOps And Automation To Promote Observability

Teams can leverage AIOps and automation to expand and enhance observability efforts in a couple of key ways:

• Enhance ecosystem observability. Teams should aggregate data from multiple sources and combine intelligence in a unified data lake. By applying machine learning to these unified data sets, teams can begin to understand interrelations among different elements. This can help teams can move beyond vague potential predictors of issues to a true understanding of causality, even across disparate yet interrelated systems. These insights can ultimately help teams establish observability of the complex, distributed and interrelated ecosystems that power today’s business services.

• Expand visibility. Instead of just tracking production environments, teams can pragmatically expand their automated monitoring into development and testing scenarios. In this way, teams can gain insights into the instrumentation enhancements that will further improve observability throughout the software development life cycle.

Conclusion

For IT teams contending with the dynamic, large-scale nature of modern IT environments, it is growing increasingly critical to employ observability techniques. Through observability, teams can achieve new breakthroughs in visibility and service levels. By combining observability with AIOps strategies, teams can promote not only enhanced service delivery but also accelerated digital innovation to help IT more effectively support business objectives.