CoreWeave’s Post

Platform Update: in the latest version v4.16, our focus was on the Kubernetes Cluster Autoscaler. Cluster Autoscaler is a Kubernetes add-on that automatically scales your cluster up or down based on the workload. This helps optimize resource utilization, reduce costs, and improve performance. More details can be found here: https://lnkd.in/eCUjtiJ5 https://lnkd.in/eDYHTTwa #swiss #cloudcomputing #kubernetes #k8s #autoscaling

You invested in containerization and #kubernetes to reap the benefits of elasticity, resiliency, and speed to market. But traditional metrics like availability and latency don’t solve for performance in dynamic multi-cloud or containerized environments. IBM Turbonomic automatically determines the right resource allocation actions—and when to make them—to help ensure your Kubernetes environments and mission-critical apps get exactly what they need to meet your SLOs.

Application Gateway for Containers is now Generally Available. Much awaited service for Layer 7 load balancing for workload running in Kubernetes Cluster.

The Container Runtime In order to be able to run the containers on the nodes, it is necessary to have a #ContainerRuntime installed on each of the nodes. The Container Runtime is responsible for running the containers on the nodes. When you are using #Docker or #Podman to run containers on your machine, for example, you are making use of some Container Runtime, or rather, your Container Engine is making use of some Container Runtime. We have three types of Container Runtime: Low-level: These are the Container Runtimes that are executed directly by the Kernel, such as runc, crun and runsc. High-level: These are the Container Runtimes that are run by a Container Engine, such as containerd, CRI-O, and Podman. Sandbox: are the Container Runtimes that are executed by a Container Engine and that are responsible for running containers securely in unikernels or using some proxy to communicate with the Kernel. gVisor is an example of a Sandbox-type Container Runtime. Virtualized: These are the Container Runtimes that are run by a Container Engine and that are responsible for running containers securely on virtual machines. The performance here is a little lower than when we have one running natively. Kata Containers is an example of a Virtualized Container Runtime.

Tired of your Kubernetes apps running out of juice? 🤔 Let's dive into the world of resource management! ✪ Kubernetes Resource Management: 1️⃣ Kubernetes resource management is crucial for deploying and managing containerized applications. 👨💻 2️⃣ It allows administrators to control the allocation of CPU, memory, storage, and other resources. 💡 #Kubernetes Resource ✪ Compute and Non-Compute Resources: 1️⃣ Compute resources include CPU, memory, and ephemeral storage. 💻 2️⃣ Non-compute resources encompass network bandwidth, disk IOPS, and GPU acceleration. 🚀 #Resource ✪ Importance of Resource Management: 1️⃣ Efficient resource management ensures applications have enough resources. 📊 2️⃣ It prevents excessive consumption and enables scheduling decisions based on resource requirements. ⏳ 3️⃣ Resource management also optimizes infrastructure cost, ensuring optimal resource utilization and high-performance applications in Kubernetes clusters. 💰 #Resource Management ✪ Kubernetes Resources: 1️⃣ Cluster resources include CPU, memory, storage, and network bandwidth. 🌐 2️⃣ Pod resources are allocated to individual pods with CPU, memory, and volume storage. 📦 #Kubernetes Resources CTA: Ready to level up your Kubernetes resource game? Let's discuss strategies in the comments! 👇 P.S. Resource requests and limits are your secret weapons – use them wisely! 😉 Rahul Maheshwari #kubernetes #containerization #devops #cloudnative #resourceoptimization

🎥 Launching the first video in new 𝐕𝐢𝐫𝐭𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐒𝐞𝐫𝐢𝐞𝐬 with 𝐈𝐁𝐌 𝐒𝐓𝐎𝐑𝐀𝐆𝐄 𝐅𝐔𝐒𝐈𝐎𝐍! ✨ "Unified Mastery: Bridging Virtual Machines and Containers with IBM Storage Fusion" ✨ ➡️ Explore the need for a unified platform for containers and virtual machines. 🌉 Discover how IBM Storage Fusion provides a robust bridge for running containers and VMs on the same platform seamlessly. 🔧 Get a step-by-step guide on installing the OpenShift Virtualization Operator and setting up a HyperConverged Instance. 🖥️ Experience the creation of the first Virtual Machine. 🔗 [ https://lnkd.in/dRdGNN97 ] 𝐃𝐢𝐬𝐜𝐥𝐚𝐢𝐦𝐞𝐫: The views and opinions expressed in this guide are solely my own and do not reflect those of my employer. This is shared for informational purposes only and should not be considered as official guidance or advice. Shajeer Mohammed Rahul Nema Garth Tschetter Rob Coventry Pallavi Singh Hemant Thopate Ashish Chaurasia Vaibhav Telang Sandeep Patil Kiran Kothule ROHAN SHINDE AYUSH PATNI Sumant Padbidri Brandon Mann Sathya Ramadas Deepak Mane #Virtualization #IBMStorageFusion #ITInfrastructure #Containers #VMs #TechSolutions #Innovation #TechTalk #YouTubeSeries #TechHorizonHub

Why is container orchestration critical to your organization for portability, scalability and resource efficiency? Learn the ins and outs of containers and how #Kasten K10 can help you overcome roadblocks in your #Kubernetes journey in this webinar replay: https://hubs.la/Q0229sql0

𝐅𝐢𝐧𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐁𝐚𝐥𝐚𝐧𝐜𝐞: 𝐖𝐡𝐞𝐧 𝐭𝐨 𝐂𝐡𝐨𝐨𝐬𝐞 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐨𝐫 𝐕𝐌𝐬 Achieving the right equilibrium between Kubernetes and Virtual Machines (VMs) is a key decision in modern infrastructure management. While Kubernetes offers immense flexibility, there are instances where it might not be the ideal choice. One common pitfall is attempting to containerize a monolithic application, often referred to as an 'elephant' and deploying it on massive Kubernetes nodes. In such scenarios, opting to keep it running as a Virtual Machine (VM) can be a more practical and efficient solution. The presence of Kubernetes in your toolkit doesn't mean that every workload must run within its realm. VMs continue to hold their place in specific use cases, and understanding when to leverage each technology is essential for achieving optimal resource utilization and performance. #kubernetes #openshift #tanzu #rancher #gke #eks #aks

Unlocking the Power of Infrastructure: 🚀 Exploring the nuances of running applications in diverse environments. From the raw performance of bare metal 💻, the flexibility of virtual machines 🔄, to the streamlined efficiency of containerized environments 🐳 – each ecosystem has its unique strengths. Let's navigate the terrain where hardware meets virtualization, and containers redefine scalability. Embrace the future of application deployment with insights into the heart of computing infrastructure! These are the difference of running applications in bare metal virtual machine and Containerised Environment 1️⃣ Bare Metal: Unleash raw power! Run applications directly on dedicated hardware for maximum performance. Ideal for resource-intensive tasks and demanding workloads. 💪 2️⃣ Virtual Machines: Flexibility meets isolation! Create virtual instances on a hypervisor, allowing multiple OS environments on a single physical server. Perfect for efficient resource utilization and diverse application needs. 🔄 3️⃣ Containerized Environment: Streamlined efficiency! Bundle applications and their dependencies into lightweight containers, enabling seamless deployment across various environments. Embrace scalability and portability with Docker, Kubernetes, and more. 🚀 Each mode has its strengths—choose wisely based on your specific requirements and usher in a new era of application deployment #BareMetal #Virtualization #containers #docker #kubernetes #application

As a K8s operator, you could be losing at least $15,640.64 in as little time as 5 days. That amounts to a $ 114,1766.72 loss per year. And this is only for 5 clusters. - In an event, KubeCon Europe in Amsterdam, over 10,000 attendees gathered to discuss the increasingly popular computing platform, Kubernetes (K8s) and its rising costs. - The cluster cost calculated per five days was $123,814.64. The average cost of the other clusters was $10,817.40 per day. - This was only for 5 clusters out of 35 total clusters. The higher the number of clusters, the higher the cost. - The reason for these shooting costs turned out to be lack of visibility into Kubernetes clusters, which remains one of the biggest challenges for all engineers and K8 leadership to date. - Drilling further down, the key challenges for K8s are: - Effective troubleshooting  - Ensuring functionality of high-deployment services  - Balancing security, engineering resources, and customer needs - Keep abreast of security and compliance How to manage all that and keep Kubernetes cloud costs under control while at the same time, gaining a good visibility of your Kubernetes clusters? You can find answers to all these questions in the webinar. Joining us today is Natan Yellin, the CEO of Robusta Dev, a pioneering K8 service provider. Joining him in conversation is Abdullah Shah, the CEO of Xgrid, a transformative organization, solving client needs in this department for over a decade. Natan’s valuable insights will turn your Kubernetes clusters into cost-efficient powerhouses while maintaining top-notch performance and security. https://lnkd.in/eCZGJXNB