Observability—taking ‘monitoring’ to the next level

 A blog post by David Linthicum, chief cloud strategy officer, Cloud Services, Deloitte Consulting LLP

As multi-cloud and hybrid cloud architectures become the new norm, many organizations are realizing a need to have a more meaningful way to observe system behavior and react, auto-heal, and optimize system performance. That’s where the emerging discipline of Observability comes in. Observability is the process of monitoring the internal state of complex systems—in real time—with AI and machine learning tools along with logs, tracing, and metrics and inferring performance from the data they gather.

With Observability, Ops practitioners can not only discover systems issues in real time; they can also anticipate the future behavior of those systems using data analysis and other emerging technologies. The goal of Observability is for IT operations to become proactive in finding and fixing performance issues.

Observability versus monitoring

Observability goes beyond traditional monitoring of systems and their behavior. While monitoring involves examining predefined metrics, logs, and tracing activities to find and fix problems after they occur, Observability provides the ability to proactively find problems as systems operate, to debug in real time, and to gain meaningful insights into performance.

These deeper insights into performance enable cloud practitioners to predict future states and solve issues before they become critical. The upshot? Observability takes monitoring, management, operations, security, and governance to the next level.

Why is Observability so important now?

The nature of how new applications are built has changed significantly. Cloud-based applications depend on multiple servers, mostly RESTful APIs, and microservices. That means cloud practitioners are dealing with massive complexity. Serverless and service-based applications, and certainly container-based applications, also provide little visibility into the underlying infrastructure.

So if, for example, a processer is failing, or if the network device is failing, Observability—implemented using practices like AIOps and application management systems—can help Ops personnel find and fix those issues as they’re occurring—not after the fact, when they may have had a severe impact.

Further, many companies have highly distributed applications that reside on a multi- or hybrid cloud architecture and that operate with containers and microservices giving them the ability to scale up and scale down almost instantaneously. Many of those applications can potentially support millions of simultaneous users. Observability provides the capability to proactively monitor and have a more holistic view of those applications and how well their behavior is supporting users and, more importantly, to spot issues quickly and fix them proactively.

Finally, part of the notion of Observability is the reliance on modern tools, such as AI and machine learning, to enable companies to better understand the properties and behaviors of their applications. This includes application performance and dealing with underlying complexities, distribution, and the dependencies that are part of those applications. So, companies need to understand not only how to monitor a particular instance of how an application is running but also how various components are linked together and orchestrated to drive healthy systems.

Observability provides the ability to view holistically how applications work in their own ecosystems. And it goes further, helping practitioners understand the interdependency of ecosystems that have to work together to keep applications and IT operations healthy and running and changing moving forward.

Putting Observability into practice

The practice of Observability will be as unique as each organization that implements it. So to start, companies should determine what Observability means to them uniquely. Begin with determining value points and what enterprise systems require to run, stay secure, and become optimized—vis-à-vis cost, performance, etc. Then determine tooling needs that help implement Observability in terms of the core attributes that will provide the most value.

In essence, the practice and success of Observability rests on meeting individual requirements and implementing the appropriate technologies and solutions to meet those requirements. The ultimate goal is to create an ecosystem in which everything works in tandem to keep the application, systems, and IT operation healthy, running, and agile. 

Listen to David Linthicum in this Deloitte On Cloud Knowledge Short podcast, Observability: Taking monitoring to the next level, as he discusses the vital role cloud computing can play in sustainability initiatives.