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This is the third article in a four-part series about Autonomous Cloud Management.
In our previous posts, we talked about microservices — how they have allowed businesses to be more agile and innovative (Part One in this series) and how autonomous release intelligence helps companies take advantage of built-in quality control measures (Part 2). One thing we haven’t discussed, however, is the impact that microservices have on service level objectives (SLOs). With so many microservices, how can DevOps teams effectively manage, measure, and take appropriate action on SLOs?
Businesses use SLOs to determine an acceptable range for performance standards — and it’s up to the engineering team to set and manage SLOs. But with hundreds or sometimes thousands of microservices in a tech stack, manually setting SLOs for each is a tedious, time-consuming process. To determine the appropriate SLO, engineers must rely on reports and dashboards to track performance metrics, gathering a benchmark of service behavior in regular and peak traffic. Then, they must manually enter SLO settings for each objective they want to monitor.
It’s easy to see how the process can quickly become a time sink, tying up the engineering team’s resources and stifling innovation. It also becomes costly; paying for engineers to monitor and manage SLOs around the clock is not an inexpensive endeavor. And what happens if SLOs aren’t met? Users become frustrated by the situation — for example, when their credit card takes “too long” to go through when checking out online — and may abandon the process altogether. The business loses out to competitors, and the engineering team may be penalized.
Managing SLOs manually can be overwhelming, but thankfully there are new approaches that make it easier. Let’s take a closer look at why leading companies are turning to autonomous management of their SLOs to stay competitive and meet their service level agreements.
Instead of manually setting and managing SLOs, smart businesses are investing in a solution that autonomously helps them set, track, and remediate SLOs, ensuring that they are met. By autonomously managing important SLO indicators — like availability, latency, throughput, error rate, etc. — engineering teams will save time, and the business will be able to deliver a better experience to end users.
Additionally, autonomous microservice management lets software teams set smart SLOs for larger services, treating a related group of microservices (like those that comprise a shopping cart checkout process) holistically, managing and monitoring performance parameters together. Autonomous SLO management can also assist with release intelligence and help identify when new code degrades performance.
By choosing to set, manage, and remediate your SLOs with an autonomous solution, you’re empowering your engineering team to be innovative, focusing its resources on tasks with a higher ROI. And when combined with autonomous release intelligence, you’re positioning your software teams, your customers, and your business for the best chance of success.
In our next post, we’ll talk about the final piece of the autonomous cloud management puzzle: auto-remediations. Stay tuned.
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