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OpenShift Cluster Sizing for the Industrial Edge Pattern

Table of contents

  1. OpenShift Cluster Sizing for the Industrial Edge Pattern
    1. Table of contents
    2. Tested Platforms
    3. General OpenShift Minimum Requirements
      1. Industrial-Edge Pattern Components
      2. Industrial-Edge Pattern OpenShift Datacenter HUB Cluster Size
        1. Datacenter Cluster utilization
      3. Industrial-Edge Pattern OpenShift Factory Edge Cluster Size
        1. Factory/Edge Cluster Utilization
      4. AWS Instance Types
      5. Azure Instance Types
      6. Google Cloud (GCP) Instance Types

Tested Platforms

The Industrial-Edge pattern has been tested in the following Certified Cloud Providers.

Certified Cloud Providers 4.8 4.9
Amazon Web Services Tested Not Supported
Google Compute    
Microsoft Azure    

General OpenShift Minimum Requirements

OpenShift 4 has the following minimum requirements for sizing of nodes:

  • Minimum 4 vCPU (additional are strongly recommended).
  • Minimum 16 GB RAM (additional memory is strongly recommended, especially if etcd is colocated on masters).
  • Minimum 40 GB hard disk space for the file system containing /var/.
  • Minimum 1 GB hard disk space for the file system containing /usr/local/bin/.

There are several applications that comprise the industrial-edge pattern. In addition, the industrial-edge pattern also includes a number of supporting operators that are installed by OpenShift GitOps using ArgoCD.

Industrial-Edge Pattern Components

Here’s an inventory of what gets deployed by the Industrial-Edge pattern on the Datacenter/Hub OpenShift cluster:

Name Kind Namespace Description
line-dashboard Application manuela-tst-all Frontend application
machine-sensor-1 Application manuela-tst-all Data publisher
machine-sensor-2 Application manuela-tst-all Data publisher
messaging Application manuela-tst-all Data subscriber
mqtt2kafka-integration Application manuela-tst-all Kafka Integration
anomaly-detection-predictor-0-anomaly-detection Application manuela-tst-all Anomaly detection application
manuela-kafka-cluster-entity-operator Operator manuela-tst-all Kafka
Red Hat Advanced Cluster Management Operator open-cluster-management Advance Cluster Management
Red Hat Integration - AMQ Broker Operator manuela-tst-all AMQ Broker
Red Hat Integration - AMQ Streams Operator manuela-tst-all AMQ Streams
Open Data Hub Operator openshift-operators Open Data Hub
Red Hat OpenShift GitOps Operator openshift-operators OpenShift GitOps
Red Hat Integration - Camel K Operator manuela-tst-all Integration Platform, Kamelet Binding, Kamelet
Red Hat OpenShift Pipelines Operator All Namespaces Tekton Config, Pipelines, Triggers, Addons
Seldon Operator Operator manuela-tst-all Seldon Deployment

Industrial-Edge Pattern OpenShift Datacenter HUB Cluster Size

The Industrial-Edge pattern has been tested with a defined set of specifically tested configurations that represent the most common combinations that Red Hat OpenShift Container Platform (OCP) customers are using or deploying for the x86_64 architecture.

The Datacenter HUB OpenShift Cluster is made up of the the following on the AWS deployment tested:

Node Type Number of nodes Cloud Provider Instance Type
Master 3 Amazon Web Services m5.xlarge
Worker 4 Amazon Web Services m5.xlarge

The Datacenter HUB OpenShift cluster needs to be a bit bigger than the Factory/Edge clusters because this is where the developers will be running pipelines to build and deploy the Industrial Edge pattern on the cluster. The above cluster sizing is close to a minimum size for a Datacenter HUB cluster. In the next few sections we take some snapshots of the cluster utilization while the Industrial Edge pattern is running. Keep in mind that resources will have to be added as more developers are working building their applications.

Datacenter Cluster utilization

Below is a snapshot of the OpenShift cluster utilization while running the Industrial-Edge pattern:

CPU Memory File System Network Pod Count
13.84 Used 42.16 available of 56 73.5 GiB 146.3 GiB available of 219.8 GiB 106 GiB 732.9 GiB available of 838.9 GiB 20.65 MBps in 22.84 MBps out 354 pods

Industrial-Edge Pattern OpenShift Factory Edge Cluster Size

The OpenShift cluster is made of 3 Nodes combining Master/Workers for the Edge/Factory cluster.

Node Type Number of nodes Cloud Provider Instance Type
Master/Worker 3 Google Cloud n1-standard-8
Master/Worker 3 Amazon Cloud Services m5.2xlarge
Master/Worker 3 Microsoft Azure Standard_D8s_v3

Factory/Edge Cluster Utilization

GCP

This is a snapshot of a Google Cloud Factory Edge cluster running the production Industrial-Edge pattern.

CPU Memory File System Network Pod Count
6.55 17.45 available of 24 43.19 GiB usage 45.09 GiB available of 88.28 GiB 48.45 GiB usage 334 GiB available of 382.5 GiB 9.64 MBps in15.79 MBps out 187 pods

AWS

This is a snapshot of a Amazon Web Services Factory Edge cluster running the production Industrial-Edge pattern.

CPU Memory File System Network Pod Count
5.1 18.9 available of 24 42.91 GiB 49.27 GiB available of 92.18 GiB 51.54 GiB 308 GiB available of 359.5 GiB 9.41 MBps in 10.38 MBps out 194 pods

Azure

This is a snapshot of an Azure Factory Edge cluster running the production Industrial-Edge pattern.

CPU Memory File System Network Pod Count
7.86 15.65 available of 24 42.76 Gib used 51.15 GiB available of 94.2 GiB 71.29 GiB used 2.93 TiB available of 3 TiB 8.98 MBps in 9.64 MBps out 192 *pods

AWS Instance Types

The industrial-edge pattern was tested with the highlighted AWS instances in bold. The OpenShift installer will let you know if the instance type meets the minimum requirements for a cluster.

The message that the openshift installer will give you will be similar to this message

INFO Credentials loaded from default AWS environment variables
FATAL failed to fetch Metadata: failed to load asset "Install Config": [controlPlane.platform.aws.type: Invalid value: "m4.large": instance type does not meet minimum resource requirements of 4 vCPUs, controlPlane.platform.aws.type: Invalid value: "m4.large": instance type does not meet minimum resource requirements of 16384 MiB Memory]

Below you can find a list of the AWS instance types that can be used to deploy the industrial-edge pattern.

Instance type Default vCPUs Memory (GiB) Datacenter Factory/Edge
      3x3 OCP Cluster 3 Node OCP Cluster
m4.xlarge 4 16 N N
m4.2xlarge 8 32 Y Y
m4.4xlarge 16 64 Y Y
m4.10xlarge 40 160 Y Y
m4.16xlarge 64 256 Y Y
m5.xlarge 4 16 Y N
m5.2xlarge 8 32 Y Y
m5.4xlarge 16 64 Y Y
m5.8xlarge 32 128 Y Y
m5.12xlarge 48 192 Y Y
m5.16xlarge 64 256 Y Y
m5.24xlarge 96 384 Y Y

The OpenShift cluster is made of 4 Masters and 3 Workers for the Datacenter and the Edge/Factory cluster are made of 3 Master/Worker nodes. For the node sizes we used the m5.xlarge on AWS and this instance type met the minimum requirements to deploy the industrial-edge pattern successfully on the Datacenter hub. On the Factory/Edge cluster we used the m5.2xlarge since the minimum cluster was comprised of 3 nodes. .

To understand better what types of nodes you can use on other Cloud Providers we provide some of the details below.

Azure Instance Types

The industrial-edge pattern was also deployed on Azure using the Standard_D8s_v3 VM size. Below is a table of different VM sizes available for Azure. Keep in mind that due to limited access to Azure we only used the Standard_D8s_v3 VM size.

The OpenShift cluster is made of 3 Master and 3 Workers for the Datacenter cluster.

The OpenShift cluster is made of 3 Nodes combining Master/Workers for the Edge/Factory cluster.

Type Sizes Description
General purpose B, Dsv3, Dv3, Dasv4, Dav4, DSv2, Dv2, Av2, DC, DCv2, Dv4, Dsv4, Ddv4, Ddsv4 Balanced CPU-to-memory ratio. Ideal for testing and development, small to medium databases, and low to medium traffic web servers.
Compute optimized F, Fs, Fsv2, FX High CPU-to-memory ratio. Good for medium traffic web servers, network appliances, batch processes, and application servers.
Memory optimized Esv3, Ev3, Easv4, Eav4, Ev4, Esv4, Edv4, Edsv4, Mv2, M, DSv2, Dv2 High memory-to-CPU ratio. Great for relational database servers, medium to large caches, and in-memory analytics.
Storage optimized Lsv2 High disk throughput and IO ideal for Big Data, SQL, NoSQL databases, data warehousing and large transactional databases.
GPU NC, NCv2, NCv3, NCasT4_v3, ND, NDv2, NV, NVv3, NVv4 Specialized virtual machines targeted for heavy graphic rendering and video editing, as well as model training and inferencing (ND) with deep learning. Available with single or multiple GPUs.
High performance compute HB, HBv2, HBv3, HC, H Our fastest and most powerful CPU virtual machines with optional high-throughput network interfaces (RDMA).

For more information please refer to the Azure VM Size Page.

Google Cloud (GCP) Instance Types

The industrial-edge pattern was also deployed on GCP using the n1-standard-8 VM size. Below is a table of different VM sizes available for GCP. Keep in mind that due to limited access to GCP we only used the n1-standard-8 VM size.

The OpenShift cluster is made of 3 Master and 3 Workers for the Datacenter cluster.

The OpenShift cluster is made of 3 Nodes combining Master/Workers for the Edge/Factory cluster.

The following table provides VM recommendations for different workloads.

General purpose Workload optimized        
Cost-optimized Balanced Scale-out optimized Memory-optimized Compute-optimized Accelerator-optimized
E2 N2, N2D, N1 T2D M2, M1 C2 A2
Day-to-day computing at a lower cost Balanced price/performance across a wide range of VM shapes Best performance/cost for scale-out workloads Ultra high-memory workloads Ultra high performance for compute-intensive workloads Optimized for high performance computing workloads

For more information please refer to the GCP VM Size Page.