Autoscaling
20 articles from official documentation
Kubernetes Autoscaling: Adapting to AI Workloads
AI workloads are changing the game for Kubernetes autoscaling. With dynamic resource allocation, your autoscaler needs to consider not just node count but also the right node shape for the job. Dive into the specifics of how this impacts your deployments.
- →Understand dynamic resource allocation to specify workload needs accurately.
- →Utilize topology claims to ensure the right node shape is provisioned.
Deploying n8n on EKS: Mastering Queue Mode for Production
Running n8n in production is a challenge, especially under heavy traffic. Learn how to leverage queue mode on EKS to manage workflows effectively while ensuring reliability and scalability.
- →Implement queue mode to manage workflow efficiently under production traffic.
- →Use dedicated webhook processors to separate public and internal traffic.
Unlocking Workloads with SIG Apps: The Heart of Kubernetes
SIG Apps is crucial for managing workloads in Kubernetes, ensuring your applications run smoothly. With controllers like Deployments and StatefulSets, it defines how workloads are created and scaled. Dive in to understand its impact on your production environment.
- →Understand how SIG Apps manages core workload APIs in Kubernetes.
- →Utilize Deployments and StatefulSets for efficient application scaling.
Mastering Kubernetes HPA: Scaling Workloads to Zero
Kubernetes v1.37 introduces a game-changing feature: horizontal autoscaling down to zero replicas. This allows you to efficiently manage resources by scaling workloads based on external metrics, like queue length, even when no pods are running.
- →Configure `minReplicas` to 0 for efficient resource management.
- →Use external metrics like queue length to scale workloads dynamically.
Mastering Resource Resizing for Kubernetes Containers
Need to optimize your Kubernetes workloads? Resizing CPU and memory resources for containers can significantly enhance performance. Learn how to adjust your container specifications with precision using `kubectl` commands.
- →Request a resize by updating the desired requests and limits in the Pod's specification.
- →Use `kubectl patch`, `kubectl apply`, or `kubectl edit` to target the Pod's resize subresource.
Kubernetes v1.37: Mastering the New Features for Resilience and Efficiency
Kubernetes v1.37 introduces critical features that enhance resilience and efficiency in your clusters. Notably, the HorizontalPodAutoscaler can now scale down to zero Pods when idle, optimizing resource usage. Dive in to understand how these updates can transform your production environment.
- →Leverage HorizontalPodAutoscaler to scale down to zero Pods when idle, optimizing resource usage.
- →Utilize KYAML for clearer and less error-prone configurations in your Kubernetes manifests.
Mastering Advanced Kubernetes Control Plane Config in Amazon EKS
Unlock the full potential of your Kubernetes control plane with advanced configuration options in Amazon EKS. Learn how to optimize pod scheduling with scoring strategies like MostAllocated and LeastAllocated, and fine-tune your Horizontal Pod Autoscaler for rapid scaling.
- →Configure `nodeResourcesFit.scoringStrategy` to optimize pod placement based on your workload needs.
- →Adjust `horizontalPodAutoscalerControllerConfig.horizontalPodAutoscalerSyncPeriod` for faster scaling decisions.
Mastering EKS Auto Mode: Automated Node Failure Management
Node failures can cripple your Kubernetes clusters, but Amazon EKS Auto Mode offers a robust solution. It automatically detects, drains, and replaces failing nodes, leveraging the Node Monitoring Agent and Karpenter for seamless operation.
- →Leverage the Node Monitoring Agent to detect terminal faults and trigger node replacements.
- →Understand that Karpenter manages node replacements based on conditions set by the NMA.
Scaling Kubernetes Pods with KEDA: Mastering SQS Queue Depth
KEDA transforms how you scale Kubernetes pods by monitoring Amazon SQS queue depth. With precise configurations, it enables dynamic scaling based on real-time workload, ensuring your applications respond efficiently to demand.
- →Configure KEDA with `pollingInterval` set to 10 seconds for timely scaling actions.
- →Set `maxReplicaCount` to 30 to handle high loads effectively.
Mastering Zonal Shift Support for EKS Auto Mode and Karpenter
Zonal Shift is a game-changer for maintaining application availability during zone failures in EKS. This feature automatically manages node provisioning and traffic routing, minimizing downtime. Dive in to understand how it works and what you need to implement it effectively.
- →Enable Zonal Shift by setting the --enableZonalShift flag to true on the Karpenter controller.
- →Use the command 'aws eks update-cluster-config --name <cluster-name> --region eu-north-1 --zonal-shift-config enabled=true' to activate Zonal Shift.
Mastering Serverless with the Headlamp Plugin for Knative
Unlock the full potential of your Knative workloads with the Headlamp plugin. This tool allows you to manage KServices, traffic splitting, and autoscaling configurations all in one place. Dive in to see how it enhances your serverless experience.
- →Utilize the Headlamp plugin to gain visibility into your Knative workloads.
- →Manage KServices effectively with live editing capabilities for traffic splits and autoscaling.
Unlocking EKS Auto Mode: Speed and Efficiency for Kubernetes Nodes
EKS Auto Mode is a game changer for Kubernetes scaling, delivering faster node management and smarter resource allocation. With Karpenter's 43% faster scale-out, you can optimize your cluster like never before.
- →Leverage Karpenter for 43% faster scale-out in EKS Auto Mode.
- →Increase `registryPullQPS` and `registryBurst` to 25 and 50 for optimal image pulling.
Unlocking Efficiency: Amazon EKS Auto Mode Meets Istio Ambient Mesh
Streamline your Kubernetes workloads with the powerful combination of Amazon EKS Auto Mode and Istio Ambient Mesh. This integration automates node management while providing seamless mutual TLS encryption across your services. Discover how to leverage these technologies for enhanced security and performance.
- →Automate node provisioning and scaling with Amazon EKS Auto Mode.
- →Implement mutual TLS encryption seamlessly using Istio Ambient Mesh.
Scaling StarRocks on EKS: Harnessing KEDA and Karpenter for OLAP Power
Unlock the full potential of your OLAP workloads with StarRocks on Amazon EKS. Learn how KEDA and Karpenter enable near-instant scaling of compute resources while maintaining a cost-effective shared-data architecture.
- →Utilize KEDA for autoscaling BE and CN nodes based on Prometheus metrics.
- →Deploy BE nodes as StatefulSets for durable storage using Amazon EBS volumes.
Scaling StarRocks on EKS: Harnessing KEDA and Karpenter for OLAP Efficiency
In the world of enterprise OLAP workloads, scaling efficiently is crucial. By leveraging KEDA for autoscaling and Karpenter for node provisioning on Amazon EKS, you can dynamically adjust your StarRocks cluster to meet fluctuating query demands without data movement.
- →Leverage KEDA for near-instant scaling of compute resources based on Prometheus metrics.
- →Utilize the StarRocks Kubernetes Operator for automated cluster lifecycle management.
GPU Autoscaling in Kubernetes: Mastering KEDA with External Scalers
Unlock the power of GPU autoscaling in Kubernetes with KEDA. Learn how to build a custom external scaler that reads GPU metrics via NVML and drives Horizontal Pod Autoscaler (HPA) decisions. This is essential for optimizing resource usage in GPU-heavy workloads.
- →Build a custom DaemonSet to read GPU metrics using NVML.
- →Serve GPU metrics over gRPC with KEDA's ExternalScaler interface.
Unlocking Efficiency with Amazon EKS Auto Mode: Strategies for Control and Optimization
Amazon EKS Auto Mode is a game changer for Kubernetes management, automating everything from provisioning to patching. With just-in-time scaling, it dynamically adjusts resources based on workload demands, minimizing operational overhead.
- →Automate cluster management to reduce operational overhead.
- →Leverage just-in-time scaling to provision capacity based on workload demands.
Kubernetes v1.36: Mastering In-Place Vertical Scaling for Pods
Kubernetes v1.36 introduces a game-changing feature: in-place vertical scaling for pod-level resources. This allows you to adjust resource budgets without container restarts, streamlining your operations. Dive into how this works and what you need to know to leverage it effectively.
- →Utilize the InPlacePodLevelResourcesVerticalScaling feature to adjust resource budgets without restarts.
- →Set the restartPolicy to NotRequired for minimal disruption during resource updates.
KEDA in Action: Dynamic Autoscaling for Kubernetes
KEDA transforms how you scale applications in Kubernetes by responding to real-world events. With components like ScaledObjects and TriggerAuthentication, it offers a robust solution for dynamic resource management.
- →Utilize ScaledObjects to link your app to external event sources for dynamic scaling.
- →Configure TriggerAuthentication to securely access event sources with environment variables or cloud-specific credentials.
HPA in Production: What the Docs Don't Tell You
Scaling workloads in Kubernetes is crucial for performance and cost efficiency. The Horizontal Pod Autoscaler (HPA) automatically adjusts the number of pods based on CPU utilization, but there are nuances to consider. Dive into the specifics of HPA and how to avoid common pitfalls.
- →Configure HPA with a target average CPU utilization of 50% for optimal performance.
- →Set minimum and maximum replicas to control scaling behavior effectively.
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