Hybrid Cloud Patterns

OpenShift Cluster Sizing for the Retail Pattern

Tested Platforms

The retail pattern has been tested in the following Certified Cloud Providers.

Certified Cloud Providers4.10
Amazon Web Services:heavy_check_mark:
Microsoft Azure
Google Cloud Platform

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 retail pattern. In addition, the retail pattern also includes a number of supporting operators that are installed by OpenShift GitOps using ArgoCD.

Retail Pattern OpenShift Datacenter HUB Cluster Size

The retail 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 TypeNumber of nodesCloud ProviderInstance Type
Master3Amazon Web Servicesm5.xlarge
Worker3Amazon Web Servicesm5.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.

Retail Pattern OpenShift Store Edge Cluster Size

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

Node TypeNumber of nodesCloud ProviderInstance Type
Master/Worker3Google Cloudn1-standard-8
Master/Worker3Amazon Cloud Servicesm5.2xlarge
Master/Worker3Microsoft AzureStandard_D8s_v3

AWS Instance Types

The retail 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 retail pattern.

Instance typeDefault vCPUsMemory (GiB)DatacenterFactory/Edge
3x3 OCP Cluster3 Node OCP Cluster
m4.xlarge416NN
m4.2xlarge832YY
m4.4xlarge1664YY
m4.10xlarge40160YY
m4.16xlarge64256YY
m5.xlarge416YN
m5.2xlarge832YY
m5.4xlarge1664YY
m5.8xlarge32128YY
m5.12xlarge48192YY
m5.16xlarge64256YY
m5.24xlarge96384YY

The OpenShift cluster is made of 3 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 retail 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 retail 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.

TypeSizesDescription
General purposeB, Dsv3, Dv3, Dasv4, Dav4, DSv2, Dv2, Av2, DC, DCv2, Dv4, Dsv4, Ddv4, Ddsv4Balanced CPU-to-memory ratio. Ideal for testing and development, small to medium databases, and low to medium traffic web servers.
Compute optimizedF, Fs, Fsv2, FXHigh CPU-to-memory ratio. Good for medium traffic web servers, network appliances, batch processes, and application servers.
Memory optimizedEsv3, Ev3, Easv4, Eav4, Ev4, Esv4, Edv4, Edsv4, Mv2, M, DSv2, Dv2High memory-to-CPU ratio. Great for relational database servers, medium to large caches, and in-memory analytics.
Storage optimizedLsv2High disk throughput and IO ideal for Big Data, SQL, NoSQL databases, data warehousing and large transactional databases.
GPUNC, NCv2, NCv3, NCasT4_v3, ND, NDv2, NV, NVv3, NVv4Specialized 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 computeHB, HBv2, HBv3, HC, HOur 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 retail 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-optimizedBalancedScale-out optimizedMemory-optimizedCompute-optimizedAccelerator-optimized
E2N2, N2D, N1T2DM2, M1C2A2
Day-to-day computing at a lower costBalanced price/performance across a wide range of VM shapesBest performance/cost for scale-out workloadsUltra high-memory workloadsUltra high performance for compute-intensive workloadsOptimized for high performance computing workloads

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

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