AI and observability for IT operations: Does it improve performance?

In a multi-cloud, multi-data center environment, IT needs new methods for tracking and troubleshooting applications. Observability tools can provide that.

Image: iStockphoto/NicoElNino

In the early 1960s, Rudof E. Kalman, a Hungarian-American engineer, introduced the concept of system observability, defined as “a measure of how well internal states of a system can be inferred from knowledge of its external outputs.”

In IT, observability of networks and systems was facilitated by utility “fix-it” programs that came packaged with software or were internally developed, and later with network management software.

Over time, more automation and predictive analytics have been injected into the technical workflow, with network and system monitoring and alerts flagging issues (such as a failing disk drive) long before they become problems so IT can resolve them without a service interruption.

All of these are mature and successful approaches that have worked for years in the more monolithic stacks and platforms of traditional computing. However, now with the move to hybrid computing, where applications cross multiple clouds and the data center, too, it’s becoming impossible to know every step of what’s going on in an application workflow. Why? Because the network monitoring and system utilities that are in place today weren’t designed to traverse different computing platforms and trace every action. What they were (and are) good at is producing logs of what goes on in a particular system or network.

SEE: Artificial Intelligence Ethics Policy (TechRepublic Premium)

“The move from monolithic apps to microservices can be overwhelming,” said Charlotte Dunlap, principal analyst for application platforms, enterprise technology and services at GlobalData PLC. “A major challenge is the almost impossible task of monitoring and optimizing applications running within containerized environments such as Kubernetes. As a result, participants of DevOps efforts are forced to reexamine how they implement observability earlier in the application life cycle to improve insight in the underlying infrastructure.”

Corporate IT departments need to address this problem if they are to achieve total visibility (and troubleshooting capability) in a multi-cloud and data center environment. This is where observability  software comes in.

Like network and system monitoring software, observability software will provide you with logs that enable you to troubleshoot issues in every system and network silo. However,  observability software goes further by availing a full tracing of every application’s activity and components along a hybrid network of multiple data centers and clouds that an app might be using. Along the way, the observability software measures the performance of each application component.

SEE: Metaverse cheat sheet: Everything you need to know (free PDF) (TechRepublic)

The end result is that IT gets logs and performance metrics for how each application component on a hybrid chain of cloud and data center IT infrastructure is performing—and it also gets a detailed activity log for each component.

This restores end to end visibility of application performance in a hybrid cloud and data center infrastructure, and enables  IT system programmers and network administrators to resolve and tune application performance faster. A layer of artificial intelligence (AI) can also be added to observability that speeds time to problem resolution because it screens out  ”noise” that system and network alerts would flag, but that are really non-issues for problem resolution.

Now it’s up to IT to adopt these new tools so 360-degree visibility can be re-established for  applications in a multi-cloud and data center environment. Statista estimates that by 2024, the global observability market will be at $19.8 billion, so it looks like companies are getting the message.

Source link

Leave a Comment

Your email address will not be published.