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]Tested Platforms
The Medical Diagnosis pattern has been tested in the following Certified Cloud Providers.
| Certified Cloud Providers | 4.8 | 4.9 | 4.10 | 4.11 |
|---|---|---|---|---|
Amazon Web Services | Tested | Tested | Tested | Tested |
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 Control Planes).
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/.
Medical Diagnosis Pattern Components
Here’s an inventory of what gets deployed by the Medical Diagnosis pattern:
| Name | Kind | Namespace | Description |
|---|---|---|---|
Medical Diagnosis Hub | Application | medical-diagnosis-hub | Hub GitOps management |
Red Hat OpenShift GitOps | Operator | openshift-operators | OpenShift GitOps |
Red Hat OpenShift Data Foundations | Operator | openshift-storage | Cloud Native storage solution |
Red Hat AMQStreams (Apache Kafka) | Operator | openshift-operators | AMQ Streams provides Apache Kafka access |
Red Hat OpenShift Serverless | Operator | - knative-eventing - knative-serving | Provides access to knative eventing and serving functions |
Medical Diagnosis Pattern OpenShift Cluster Size
The Medical Diagnosis pattern has been tested with a defined set of 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 OpenShift cluster for the Medical Diagnosis pattern needs to be sized a bit larger to support the compute and storage demands of OpenShift Data Foundations and other operators that make up the pattern. The above cluster sizing is close to a minimum size for an OpenShift cluster supporting this pattern. In the next few sections we take some snapshots of the cluster utilization while the Medical Diagnosis pattern is running. Keep in mind that resources will have to be added as more developers are working building their applications.
The OpenShift cluster is a standard deployment of 3 control plane nodes and 3 or more worker nodes.
| Node Type | Number of nodes | Cloud Provider | Instance Type |
|---|---|---|---|
Control Plane/Worker | 3 | Google Cloud | n1-standard-8 |
Control Plane/Worker | 3 | Amazon Cloud Services | m5.2xlarge |
Control Plane/Worker | 3 | Microsoft Azure | Standard_D8s_v3 |
AWS Instance Types
The Medical Diagnosis 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
Below you can find a list of the AWS instance types that can be used to deploy the Medical Diagnosis pattern.
| Instance type | Default vCPUs | Memory (GiB) | Hub | 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 3 Control Plane nodes and 3 Workers. For the node sizes we used the m5.4xlarge on AWS and this instance type met the minimum requirements to deploy the Medical Diagnosis pattern successfully.
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 Medical Diagnosis 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 Control Plane nodes and 3 Workers.
| Type | Sizes | Description |
|---|---|---|
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. | |
F, Fs, Fsv2, FX | High CPU-to-memory ratio. Good for medium traffic web servers, network appliances, batch processes, and application servers. | |
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. | |
Lsv2 | High disk throughput and IO ideal for Big Data, SQL, NoSQL databases, data warehousing and large transactional databases. | |
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. | |
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 Medical Diagnosis 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 Control Plane and 3 Workers 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.
