Kublr Platform and Kublr Kubernetes Cluster Minimal Hardware Recommendations

Overview

This document covers the minimal hardware recommendations for the Kublr Platform and Kublr Kubernetes cluster. After reading this article you can plan deployment of you Kublr Platform and Kublr Kubernetes cluster installation.

Kublr Kubernetes Cluster Requirement

Role Minimal required memory Minimal required CPU (cores) Components
Master node 2.5 GB 1.5 Kublr-Kubernetes master components (k8s-core, cert-updater, fluentd, kube-addon-manager, rescheduler, network, etcd, proxy, kubelet)
Worker node 1 GB 0.5 Kublr-Kubernetes worker components (fluentd, dns, proxy, network, kubelet)
Centralized monitoring agent * 2 GB 0.7 Prometheus. We recommend limit 2GB for typical installation of managed cluster which has 8 working, 40 pods per node with total 320 nodes. Retention period for prometheus agent is 1 hour.
Centralized logging agent * 0.5 GB 0.4 Rabbitmq

Kublr Platform Features Requirement

Feature Required memory Required CPU
Feature: Control Plane 1.6 GB 1
Feature: Centralized monitoring 2.9 GB 1.2
Feature: Centralized logging 11 GB 1.4
Feature: k8s core components 0.4 GB 0.1

Kublr Platform Deployment example

Single master kubernetes cluster, at one-two worker nodes, use all Kublr’s features (two for basic reliability)

For a minimal Kublr Platform installation you should have one master node with 4GB memory and 2 CPU and worker node(s) with total 10GB + 1GB × (number of nodes) and 4.4 + 0.5 × (number of nodes) CPU cores.

Please note: We do not recommend using this configuration in production but this configuration is suitable to start exploring the Kublr Platform.

Provider Master Instance Type Worker Instance Type
Amazon Web Services t2.medium/t3.medium (2 vCPU, 4GB) 2 × t2(t3) xlarge (4 vCPU, 16GB)
Google Cloud Platform n1-standard-2 (2 vCPU, 7.5GB) 2 × n1-standard-4 (4 vCPU, 15GB)
Microsoft Azure A2 v2 (2 vCPU, 4GB) 2 × A8 v2 (8 vCPU, 16GB)
On-premises 2 vCPU, 4GB 2 × VM (3 vCPU, 7GB)

Workload example

Master node: Kublr-Kubernetes master components (2.5GB, 1.5 vCPU),

Worker node 1: Kublr-Kubernetes worker components (1GB, 0.5 vCPU), Feature: ControlPlane (1.6GB, 1 vCPU), Feature: Centralized monitoring (2.9 GB, 1.2 vCPU) Feature: k8s core components (0.4 GB, 0.1 vCPU) Feature: Centralized logging (11GB, 1.4 vCPU)

Worker node 2: Kublr-Kubernetes worker components (1GB, 0.5 vCPU), Feature: Centralized logging (11GB, 1.4 vCPU)

Self-hosted Features

Kublr has several self-hosted features, which could be installed separated in Kublr-Kubernetes clusters.

Feature Required memory Required CPU
Self-hosted logging 9GB 1
Self-hosted monitoring 2.8GB 1.4

How to calculate free memory and CPU available for business applications

Note: By default Kublr disables scheduling business application on the master (you can change that), so we use only worker nodes in our formula.

Available memory = (number of nodes) × (memory per node) - (number of nodes) × 1GB - (has Self-hosted logging) × 9GB - (has Self-hosted monitoring) × 2.9GB - 0.4 GB - 2GB (Central monitoring agent per every cluster) - 0.3 (Central logging agent per every cluster).

Available CPU = (number of nodes) × (vCPU per node) - (number of nodes) × 0.5 - (has Self-hosted logging) × 1 - (has Self-hosted monitoring) × 1.4 - 0.1 - 0.7 (Central monitoring agent per every cluster) - 0.3 (Central logging agent per every cluster).

Example

User wants to create a Kublr-Kubernetes cluster with 5 n1-standard-4 nodes (in Google Cloud Platform) with enabled self-hosted logging, but disabled self-hosted monitoring, then:

  • Available memory = 5 × 15 - 5 × 1 - yes ×9 - no × 2.8 - 0.4 - 2 - 0.3 = 58.3GB.
  • Available CPU = 5 × 4 - 5 × 0.5 - yes × 1 - no × 1.4 - 0.1 - 0.7 - 0.3 = 15.4 vCPUs.

Note: You will use centralized monitoring available in the Kublr Platform instead of self-hosted monitoring


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