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Rightsizing resource requests is an increasing challenge for teams using Kubernetes—and especially critical as they scale their environments. Overprovisioning CPU and memory lead to costly overspending, but underprovisioning risks CPU throttling and out-of-memory errors if requested resources aren’t sufficient. Dev and engineering teams that don’t thoroughly understand the live performance profile of their containers will usually play it safe and request vastly more CPU and memory resources than required, often with significant budget waste.

The open source Kubecost tool (https://github.com/kubecost) has had a Request Sizing dashboard to help Kubernetes users bring more cost efficiency to their resource requests. One of the tool’s most popular optimization features, the dashboard identifies over-requested resources, offers recommendations for appropriate per-container resource requests, and estimates the cost-savings impact of implementing those recommendations. The dashboard utilizes actual usage data from live containers to provide accurate recommendations. However, leveraging the dashboard has included some hurdles, requiring users to manually update YAML requests to align resource requests with Kubecost recommendations or introduce integrations using a CD tool. 

Source de l’article sur DZONE

We operate in a continuous delivery world in which a seamless customer experience is paramount. Regardless of whether you’re a global Fortune 500 organization or a fast-growing startup, failing to deliver a digital experience that delights your users is a critical mistake you can’t afford to make.

A chief challenge compounding today’s continuous delivery expectation is the growing amount of testing that has to be carried out. In the not-too-distant past, companies controlled all of their software, available on a single platform to a similar type of user with one uniform release cycle. Today’s landscape is vastly different, with websites and apps relying on a mix of modules and services under the control of various vendors, all with independent release cycles, in a heterogeneous platform environment with a wide range of user types.

Source de l’article sur DZONE