Efficient Kubernetes scaling using Karpenter
- 4. ABOUT ME_ ● Head of Consultancy at The Scale Factory (B2B SaaS consultancy, AWS Advanced consulting partner and K8s service provider) ● Ops background, wearing different hats, engaged with many different technologies ● Open source contributor, maintainer and supporter ● HashiCorp Ambassador, OpenUK Ambassador ● Certifications and competencies: AWS, CKA, RHEL, HCTA ● Fan of automation/simplifying things, hiking and travelling @_MarkoB https://www.linkedin.com/in/marko-bevc/ | @[email protected]
- 5. KUBERNETES SCALING_ • None out of the box – manual 👩💻👨💻 • Kubernetes resources: –Pods – the smallest execution unit –Nodes – compute/instances to run Pods on –Other: storage, network, etc. @_MarkoB
- 6. HPA CONCEPT_ • Horizontal Pod Autoscaler • Adding more instances(e.g. Pods) • Doesn’t apply to non-scalable objects (e.g. DaemonSet) • Target observed metrics (i.e. average CPU or memory utilization) • Scaling out
- 7. VPA CONCEPT_ • Vertical Pod Autoscaler • Adjusting size/power (e.g. resources/limits) • “Right-sizing” your workloads to actual usage • Most commonly used on a Deployment objects • Scaling up
- 8. PODS SCALING_ • Other approaches: – HPA | VPA* (HorizontalPodAutoscaler | VerticalPodAutoscaler) – GCP: MultidimPodAutoscaler – KEDA (K8s Event Driven Autoscaling) – Knative (K8s based serverless platform)
- 9. CLUSTER AUTOSCALER_ • Industry ‘de-facto’ auto-scaling standard • Cost efficiency – automatically adjusts cluster: scale up/down • Leaning on existing Cloud building blocks • Challenges: Node Group limitations (AZ, instance type, labels), complex to use, tightly bound to the scheduler, global controller
- 10. CLUSTER AUTOSCALER SCALE-UP_ ● Reconciliation and filtering ● Scale up (in-memory simulation, <10sec) ● Expanders: random, most/least pods, price, priority ● Scale down (<10min) New Nodes Pending Pods 10 sec
- 14. size, arch, GPU, etc. @_MarkoB NODE KARPENTER SCHEDULING_ Kubernetes Control Plane
- 16. KEY CONCEPTS_ • Straightforward setup: – Provision AWS IAM Roles for Service Accounts (IRSA) – Install controllers (leader elect HA) – Apply Provisioner CRD (configuration) – one or more! – Deploy workloads • Capacity life-cycle loop: watch evaluate provision remove → → → • Well-known labels as Provisioner constraints: – kubernetes.io/arch = amd64 – kubernetes.io/os = linux – node.kubernetes.io/instance-type = m5.large – topology.kubernetes.io/zone = eu-west-1 – karpenter.sh/capacity-type = on-demand | spot ● Multi-dimension scaling (up/down and in/out)! @_MarkoB
- 17. SCALING UP_ • Provisioning and scaling • Adding more just-in-time capacity to meet demand • Early binding to nodes • Scheduling constraints: resource.requests, nodeAffinity, nodeSelector, PodDisruptionBudget, topologySpreadConstraints, inter-pod (anti-)affinity • Removing scheduler tight coupling @_MarkoB New Node Pending Pods <10 sec
- 18. SCALING IN_ @_MarkoB <10 sec Obsolete Node Pending Pods • Terminate obsolete capacity reducing costs → • Removing underutilised or empty nodes • Node TTLs (emptiness & expiration) • Consolidation • Interruption • Drift
- 19. CAPACITY CONSOLIDATION_ ● Consolidation, a.k.a off-line bin packing ● Rebalancing Node workloads based on utilisation (CPU, memory) ● Mechanisms for cluster consolidation: – Delete (on-demand | spot) – Replace (on-demand) ● Optimises for cost, minimising disruption obeying: – Scheduling constraints (PDBs, AZ affinity, topology spread constraints) – Termination grace period and expiration TTL – Instance unhealthy events and spot events (termination) ● Using least disruption when multiple Nodes that could be consolidated: – Nodes running fewer pods – Nodes that will expire soon – Nodes with lower priority Pods @_MarkoB
- 20. OTHER OPTIONS_ ● Custom User Data and AMI (i.e. Bottlerocket) ● Kubelet configuration (containerRuntime, systemReserved) ● Taints (or startupTaints) ● Control Pod Density – Network limitations ● Number of ENIs ● Number of IP addresses that can be assigned to ENI – Static Pod Density (podsPerCore) – Dynamic Pod Density (maxPods) – Limit Pod Density: topology spread, restrict instance types @_MarkoB
- 22. CONCLUSIONS_ & TAKEAWAYS ● Capacity planning is hard! 🧪 ● Key advantages: 🔥 – Flexible, lowers complexity & portable – Fast: provisioning latency <1min down to 15sec (group-less) → – Efficient: multi-dimension scaling, consolidation (delete or replace) – Adaptive: right-sizing, interruption events – Compliance (TTL)📖 ● To keep in mind: 🧑🏫 – Currently supported provider is AWS (adoption in the future?*) – Not supporting Spot Rebalance Recommendations – Careful with non-interruptable workloads, edge case of 1 replica – https://github.com/aws/karpenter/issues ➡️ ⚒️ @_MarkoB
- 23. ● Resources: – https://github.com/mbevc1/public-speaking/ – https://github.com/aws/karpenter/ – https://kubernetes.io/docs/reference/labels-annotations-taints/ – https://github.com/kubernetes/autoscaler – https://docs.aws.amazon.com/eks/latest/userguide/cluster-autoscaler.html – https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/proposals/ scalability_tests.md – https://blog.kloia.com/karpenter-cluster-autoscaler-76d7f7ec0d0e – https://blog.scaleway.com/understanding-kubernetes-autoscaling/ – https://aws.amazon.com/blogs/aws/introducing-karpenter-an-open-source-high-performance- kubernetes-cluster-autoscaler/ FURTHER READING_ @_MarkoB