# kubernetes-scaling Published articles for kubernetes-scaling. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Safe, versioned Worker Deployments on Kubernetes: Now with autoscaling! DevFeed: [Safe, versioned Worker Deployments on Kubernetes: Now with autoscaling!]() Original publisher: [Read original article]() Author: Brandon Chavis Published: 2026-03-30T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [autoscaling](), [Kubernetes](), [Deployment](), [Open Source]() Tags: [autoscaling](), [deployments](), [kubernetes](), [kubernetes-operator](), [kubernetes-scaling](), [metrics](), [product-news](), [versioning]() ### AI overview Temporal's open-source Worker Controller now supports attaching Kubernetes resources such as Horizontal Pod Autoscalers and PodDisruptionBudgets to versioned worker deployments. Worker versions can be scaled independently using version-specific backlog metrics. ### Source excerpt Automate Temporal Worker autoscaling on Kubernetes. Learn how the new WorkerResourceTemplate attaches HPAs to versioned deployments for independent scaling. ## Make Before Break - Faster Scaling Mechanics for ClickHouse Cloud DevFeed: [Make Before Break - Faster Scaling Mechanics for ClickHouse Cloud]() Original publisher: [Read original article]() Author: Ashwath Singh; Manas Alekar; Jayme Bird; Manish Gill; Francesco Ciocchetti Published: 2025-04-02T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [autoscaling](), [Kubernetes](), [Cloud](), [systems]() Tags: [architecture](), [clickhouse](), [cloud](), [cpu](), [kubernetes](), [kubernetes-scaling](), [memory](), [recommendations](), [scale](), [server](), [systems]() ### AI overview This article explains how ClickHouse Cloud redesigned its Kubernetes-based vertical scaling process. It describes the limitations of resizing replicas through rolling restarts and introduces Make-Before-Break mechanics to reduce disruption while applying new CPU and memory sizes. ### Source excerpt Read how ClickHouse Cloud reengineered Kubernetes scaling with Make-Before-Break to reduce downtime, boost flexibility, and make vertical scaling actually fast. ## Future-Proof Your AKS Cluster with Strategic IP Address Planning DevFeed: [Future-Proof Your AKS Cluster with Strategic IP Address Planning]() Original publisher: [Read original article]() Author: Sumit Prasad Published: 2024-06-25T09:12:09Z Content type: tutorial Language: en Sources: [Myntra]() Topics: [Azure Kubernetes Service](), [Azure](), [Kubernetes](), [networking](), [configuration](), [Scalability]() Tags: [aks](), [azure](), [azure-kubernetes-service](), [cni](), [configuration](), [container-networking](), [ip-address-planning](), [kubernetes](), [kubernetes-scaling](), [load-balancer](), [networking](), [scalability]() ### AI overview This article explains IP address planning for Azure Kubernetes Service clusters using default Azure CNI networking. It compares static and dynamic IP allocation and describes how pod, node, and internal load balancer scaling affects subnet capacity and resource utilization. ### Source excerpt Scaling Kubernetes efficiently is an art that blends strategic planning with technical expertise. In Azure Kubernetes Service (AKS), mastering the allocation of IP addresses is crucial for ensuring seamless scalability and optimal performance. When configuring an AKS cluster with default Azure CNI networking, it's essential to address the nature of scaling Pods, Nodes, and internal LoadBalancer-type services. This blog delves into the intricate process of IP address planning, explicitly focusing on scaling Pods, Nodes, and Services in an AKS cluster. Discover how thoughtful IP management can prevent bottlenecks, optimize resource utilization, and pave the way for a robust production environment. Image Source: https://kubernetes.io/docs/images/kubernetes-cluster-network.svg We'll talk about two approaches to IP address planning in AKS: Static IP allocation and Dynamic IP allocation. Let us understand a bit about both, and then we will go deeper to understand each approach. Static IP Allocation: Suitable for predictable and stable environments where the maximum number of pods per node is known and does not change frequently. This approach can lead to IP wastage if the number of pods varies. Dynamic IP Allocation: Better for environments with varying workloads and dynamic scaling needs. It helps reduce IP wastage by allocating additional IPs only when necessary, but it requires careful subnet planning with a buffer to accommodate dynamic allocations. Static IP Allocation The default configuration in AKS uses static IP allocation based on the 'maximum number of pods per node' setting, counting all pods created from Deployments, Daemonsets, Statefulsets, or Jobs. The private subnet CIDR specified during node pool configuration assigns IP addresses to Pods, Nodes, and Load balancer type services for an internal network. It is essential to decide the "maximum pods per node" configuration and to have a dedicated subnet CIDR range before creating a node pool. AKS reserves th