March 9, 2022
August 2, 2023
This is the second in a four-part series about Autonomous Cloud Management.
We’ve talked about the rise of microservices and how microservice architecture allows businesses to be more agile and innovative (see Part One in this series). Along with that agility and innovation comes increased responsibility. How can DevOps teams continue to ensure release quality when releases are constantly occurring?
Let’s take a closer look at release intelligence and the role it plays in keeping software safe.
Pushing a new release used to be a single activity; many changes were committed to the system simultaneously, with exhaustive testing being a built-in part of the process. But as microservices have grown more pervasive, the way software is updated has also changed. With companies pushing hundreds or even thousands of releases every day, manual load testing can stifle innovation, slowing down updates and changes and making the business far less agile and competitive.
With the rise in microservices, DevOps teams have tried to create workarounds to traditional load testing — for example, they may decide to perform load testing for only the most critical services or opt to skip load testing altogether. This presents a whole new level of risk. Because microservices are inherently intertwined, a minor issue in a single microservice can cause major problems across the system, jeopardizing your business systems’ uptime and your bottom line.
Enterprises must be able to gate what’s coming into production — the risks of not testing are simply too high. And that’s where autonomous release intelligence (RI) can help. Autonomous release intelligence allows companies to benefit on the fly from built-in quality control measures.
Autonomous release intelligence helps companies ensure application performance and reliability without the need for manual intervention. Rather than relying on developers to perform load testing, the system automatically checks the health of any new releases pushed to the production environment, gathering data on how each microservice performs.
Sedai’s autonomous release intelligence functionality actually shows development teams the software’s performance, latency, and speed, highlighting errors and deviations and assigning a deviation score. Then, it assigns each release with a score, helping the development team quickly decide if they’re ready to push the release. And if your business is using a public cloud, some RI tools are capable of also providing cost analysis, helping teams estimate a release’s potential cost savings.
Additionally, autonomous RI helps companies identify potential quality concerns within their development teams. Because autonomous release intelligence tools are able to see all updates across systems, they’re able to identify trends — like if a specific development team is consistently delivering code that doesn’t meet performance parameters.
Release intelligence is crucial for ensuring your business remains agile and innovative, but it isn’t the only factor you need to consider in a microservice-rich development environment. Stay tuned for our next post, where we’ll discuss the best ways to manage service level objectives autonomously.
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