Leaseweb’s Post

We're excited to announce that you can now optimize the control plane components of your Kubernetes clusters with the Vertical Pod Autoscaler (VPA) 😀 Leaseweb Product Manager, Govinnd Cherukat, explains exactly what is being enhanced and how this benefits you in his blog post below. https://lnkd.in/g64FdakC

🌟 75-Day DevOps Challenge: Week 5 - Advanced Orchestration, Multi-Cloud, and Security 🌟 🚀 Day 29: Advanced Kubernetes - Horizontal Pod Autoscaling (HPA) Let’s scale our applications dynamically with Horizontal Pod Autoscaling (HPA) in Kubernetes, which automatically adjusts the number of pod replicas based on CPU or memory usage. 🔸 Goal: Set up HPA for a deployed application in your Kubernetes cluster. 🔸 Challenge: Test the autoscaling by simulating high traffic loads and observe how pods scale. #KubernetesAutoscaling #HPA #CloudDevOps #DevOpsJourney #75DayChallenge

How does a HorizontalPodAutoscaler work? Kubernetes implements horizontal pod autoscaling as a control loop that runs intermittently (it is not a continuous process). The interval is set by the --horizontal-pod-autoscaler-sync-period parameter to the kube-controller-manager (and the default interval is 15 seconds). #autoscaler #k8s #kubernetes

VPA (Vertical Pod Autoscaler) is a powerful tool, but it has some drawbacks and limitations that you should know about before using it in your production environment. Download your copy of a guide, written by Daniele Polencic, Managing Director of Learnk8s, that discusses the VPA's functionality and application across various deployment scenarios. You'll get practical, "how-to" advice for configuring the VPA to avoid common pitfalls and unlock its full potential. This is a must-read for anyone who is using Kubernetes. https://lnkd.in/eSCdnjFW

Whenever you think of VPA (Vertical Pod Autoscaler) in #kubernetes, it's not about just comparing it with HPA (Horizontal Pod Autoscaler). Apart from complimenting HPA for overall better efficiency, VPA is a powerful tool which has various use cases to support in any production environment. To know it better, download your copy of the guide. #perfectscale #learnk8s #kubernetes #efficiency #scaling #cloud

Need to design flexible Kubernetes cluster footprints for hybrid, edge, and other advanced use-cases? K0smotron is the solution you’re looking for! It’s a ClusterAPI multi-provider and operator that gives you crazy flexibility in configuring distributed k8s clusters with hosted or conventional control planes, and lifecycle managing them on any infrastructure, all through the Kubernetes API. Watch our Tech Talk - How to Create Scalable Control Planes with k0smotron for Kubernetes to learn more! https://lnkd.in/dt5TKnCT #kubernetes #k8s #LensIDE

Do you know how much you are saving by switching to virtual clusters? With the latest vCluster Platform v4.2 release, you no longer have to guess. Introducing the Cost Savings Dashboard, a built-in feature that tracks your cost data and gives you a clear view of how much you’re saving by using virtual clusters instead of traditional Kubernetes clusters. 1️⃣ Reduced Clusters: Save big—virtual clusters cost ~$90/year vs. $900/year for traditional clusters. 2️⃣ Sleep Mode: Automatically cut costs for idle clusters. 3️⃣ Shared Platform Stack: Share components across virtual clusters to save even more (Coming Soon) We can’t wait to hear what you think about these new features. For more details, check out our changelog: https://lnkd.in/gYb8sZPM

K0s Brings the power of Kubernetes to Your Edge Devices

Kubernetes v1.30 is called "uwubernetes". Meanwhile, in K8s v1.30, ContainerResource metrics autoscaling will become a stable feature. This feature allows the Horizontal Pod Autoscaler (HPA) to automatically scale based on individual container resource usage, rather than the combined resource usage of all containers in a pod.

Discover the benefits of using Kubernetes-based Horizontal Pod Autoscaling (HPA) to automatically adjust the number of pod replicas based on workload demands, ensuring optimal resource utilization.