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OpenShift users have many options when it comes to deploying a Red Hat OpenShift cluster in AWS. In Red Hat OpenShift Container Platform 4.12, we introduced the manual steps to extend cluster nodes into Amazon Web Services (AWS) Local Zones when installing in existing VPC. We are pleased to announce in OpenShift 4.14 the fully IPI (installer-provisioned infrastructure) automation to extend worker nodes to AWS Local Zones.

What is AWS Local Zones?

Local Zones allow you to use selected AWS services, like compute and storage services, closer to the metropolitan region, and end-users, than the regular zones, providing them with very low latency access to the applications running locally. Local Zones are fully owned and managed by AWS with no upfront commitment and no hardware purchase or lease required. In addition, Local Zones connect to the parent AWS cloud region via AWS' redundant and very high-bandwidth private network, providing applications running in Local Zones fast, secure, and seamless access to the rest of AWS services.


Figure 1: AWS Infrastructure Continuum

OpenShift and AWS Local Zones

Using OpenShift with Local Zones, application developers and service consumers might have the following benefits:

  • Improving application performance and user experience by hosting resources closer to the user. Local Zones reduce the time it takes for data to travel over the network, resulting in faster load times and more responsive applications. This is especially important for applications, such as video streaming or online gaming, that requires low-latency performance and real-time data access.
  • Saving costs by avoiding data transfer charges when hosting resources in specific geographic locations, whereby customers avoid high costs associated with data transfer charges, such as cloud egress charges, which is a significant business expense, when large volumes of data are moved between regions in the case of image, graphics, and video-related applications.
  • Providing to regulated industries, such as healthcare, government agencies, financial institutions, and others, a way to meet data residency requirements by hosting data and applications in specific locations to comply with regulatory laws and mandates.

AWS Local Zones limitations in OpenShift

There are a few limitations in the current AWS Local Zones offering that require attention when deploying OpenShift:

  • The Maximum Transmission Unit (MTU) between an Amazon EC2 instance in a Local Zone and an Amazon EC2 instance in the Region is 1300. This causes the overlay network MTU to change according to the network plugin that is used on the deployment.
  • Network resources such as Network Load Balancer (NLB), Classic Load Balancer, and Nat Gateways are not globally available in AWS Local Zones, so the installer will not deploy those resources automatically.
  • The AWS Elastic Block Storage (EBS) volume type gp3 is the default for node volumes and the storage class set on regular AWS OpenShift clusters. This volume type is not globally available in Local Zones locations. By default, the nodes running in Local Zones are deployed with the gp2 EBS volume type, and the gp2-csi StorageClass must be set when creating workloads into those nodes.

Installing an OpenShift cluster with AWS Local Zones

This section describes how to deploy OpenShift compute nodes in Local Zones at cluster creation time, where the OpenShift Installer fully automates the cluster installation including network components in configured Local Zones.

The following diagram plots the infrastructure components created by the IPI installation with worker nodes in the Local Zone:

  • One regular VPC, and subnets on each Availability Zone in the Region.
  • One standard OpenShift Cluster in us-east-1 with three Control Plane nodes, and three Compute nodes.
  • Public and private subnets in the Local Zone in the New York metropolitan region (us-east-1-nyc-1a).
  • One edge compute node in the private subnet in the zone us-east-1-nyc-1a;

Figure 2: OpenShift Cluster installed in us-east-1 extending nodes to Local Zone in New York

To deploy an OpenShift cluster extending compute nodes in Local Zone subnets, it is required to define the edge compute pool in the install-config.yaml file, not enabled by default.

The installation process creates the network components in the Local Zone classifying those as "Edge Zone", creating MachineSet manifests for each location. See the OpenShift documentation for more details.

Once the cluster is installed, the label is set on each node located in the Local Zone, along with the default label


Install the clients:

Step 1. Enable the AWS Local Zone Group

The zones in Local Zones are not enabled by default, the zone group needs to opt-in before creating resources in those locations.

You can list the available Local Zones and the attributes using the operation DescribeAvailabilityZones with AWS CLI:

aws --region us-east-1 ec2 describe-availability-zones \
--query 'AvailabilityZones[].[{ZoneName: ZoneName, GroupName: GroupName, Status: OptInStatus}]' \
--filters Name=zone-type,Values=local-zone \

To enable the Local Zone in New York (used in this post), run:

aws ec2 modify-availability-zone-group \
--group-name "us-east-1-nyc-1" \
--opt-in-status opted-in

Step 2. Create the OpenShift Cluster

Create the install-config.yaml setting for the AWS Local Zone name in the edge compute pool:

apiVersion: v1
publish: External
baseDomain: "<CHANGE_ME: Base Domain>"
name: demo-lz
- name: edge
- us-east-1-nyc-1a
region: us-east-1
pullSecret: '<CHANGE_ME: pull-secret-content>'
sshKey: |
'<CHANGE_ME: ssh-keys>'

Create the cluster:

./openshift-install create cluster

That's it, the installer program creates all the infrastructure and configuration required to extend worker nodes in the selected location.

Once the installation is finished, review the EC2 worker node status provisioned by Machine API:

export KUBECONFIG=$PWD/auth/kubeconfig
./oc get machines -l -n openshift-machine-api
NAME                                        PHASE     TYPE          REGION      ZONE               AGE
demo-lz-tvqld-edge-us-east-1-nyc-1a-scgjl   Running   c5d.2xlarge   us-east-1   us-east-1-nyc-1a   21m

You can also check the nodes created in AWS Local Zones after the machine is in the Running phase, labeled with .

./oc get nodes -l
NAME                           STATUS   ROLES         AGE     VERSION
ip-10-0-194-188.ec2.internal   Ready    edge,worker   5m45s   v1.27.3+4aaeaec

Figure 3: OpenShift nodes created by the installer in AWS EC2 Console

The cluster is installed and is ready to run workloads in Edge Compute nodes.

The following links describe more activities allowed to enhance OpenShift and Local Zones:

Benchmarking the application connection time

To validate the network improvement when delivering the application closer to the user, we expanded the cluster installed in the last section to create a node in a new Local Zone us-east-1-bue-1a .

The tests measure client connectivity to the application endpoint deployed in the cluster from different locations on the internet, some are closer to the metropolitan regions covered by nodes deployed in Local Zones, measuring the network benefits when using edge compute pools in OpenShift.

The image below shows an overview of the tested environment:

  • three clients testing three endpoints (servers)
  • clients are originating connections from New York (US), London (UK), South Brazil
  • application distributed into different locations in the same cluster: Local Zone New York (us-east-1-nyc-1a), Local Zone Buenos Aires (us-east-1-bue-1a), and in the availability zone in the region (us-east-1)

Figure 4: User Workloads in Local Zones


The tests generate requests from different clients, extracting the curl variables writing it out to the console.

Create the script to test the endpoints:

cat <<EOF >
#!/usr/bin/env bash
echo "# Client Location:"
curl -s\$(curl -s |jq -r '[.city, .countryCode]'

run_curl() {
echo -e "time_namelookup\t time_connect \t time_starttransfer \t time_total"
for idx in \$(seq 1 5); do
curl -sw "%{time_namelookup} \t %{time_connect} \t %{time_starttransfer} \t\t %{time_total}\n" \
-o /dev/null -H "Host: \$1" \${2:-\$1}

echo -e "\n# Collecting request times to server running in AZs/Regular zones \n# [endpoint ${APP_HOST_AZ}]"
run_curl ${APP_HOST_AZ}

echo -e "\n# Collecting request times to server running in Local Zone NYC \n# [endpoint ${APP_HOST_NYC}]"
run_curl ${APP_HOST_NYC}

echo -e "\n# Collecting request times to server running in Local Zone BUE \n# [endpoint ${APP_HOST_BUE}]"
run_curl ${APP_HOST_BUE} ${IP_HOST_BUE}


Copy and run to the clients, then run it:


Results by client:

  • Results of the client located in the region of US/New York:
# Client Location: 
["North Bergen", "US"]

# Collecting request times to server running in AZs/Regular zones
# [endpoint]
time_namelookup time_connect time_starttransfer time_total
0.010444 0.018078 0.031563 0.032449
0.012000 0.019141 0.030801 0.031725
0.000777 0.007918 0.019087 0.019860
0.001437 0.008690 0.020179 0.020955
0.005015 0.011915 0.023527 0.024309

# Collecting request times to server running in Local Zone NYC
# [endpoint]
time_namelookup time_connect time_starttransfer time_total
0.002986 0.005248 0.009702 0.010571
0.001368 0.003183 0.006447 0.007270
0.001100 0.002503 0.005711 0.006586
0.003174 0.004643 0.007955 0.008725
0.003144 0.004601 0.007663 0.008500

# Collecting request times to server running in Local Zone BUE
# [endpoint]
time_namelookup time_connect time_starttransfer time_total
0.000026 0.141142 0.284566 0.285606
0.000027 0.141474 0.284454 0.285362
0.000025 0.141334 0.284213 0.285045
0.000023 0.141085 0.283620 0.284515
0.000026 0.141586 0.284625 0.285490


  • Results of the client located in the region of UK/London:
# Client Location: 
["Enfield", "GB"]

# Collecting request times to server running in AZs/Regular zones
# [endpoint]
time_namelookup time_connect time_starttransfer time_total
0.014856 0.096285 0.181679 0.182629
0.001565 0.079669 0.162386 0.163355
0.001891 0.081879 0.165896 0.166834
0.001465 0.080108 0.163756 0.164491
0.001224 0.081282 0.165828 0.166998

# Collecting request times to server running in Local Zone NYC
# [endpoint]
time_namelookup time_connect time_starttransfer time_total
0.002339 0.092894 0.184171 0.185058
0.001506 0.085278 0.167627 0.168613
0.001176 0.083452 0.167570 0.168474
0.001483 0.092990 0.186173 0.186859
0.001130 0.083462 0.167462 0.168527

# Collecting request times to server running in Local Zone BUE
# [endpoint]
time_namelookup time_connect time_starttransfer time_total
0.000046 0.229893 0.462351 0.463439
0.000030 0.233715 0.468338 0.469316
0.000057 0.230159 0.462013 0.463272
0.000041 0.230470 0.462181 0.463116
0.000044 0.228971 0.459642 0.460627


  • Results of the client located in the region of Brazil/South:
# Client Location: 
["Florianópolis", "BR"]

# Collecting request times to server running in AZs/Regular zones
# [endpoint]
time_namelookup time_connect time_starttransfer time_total
0.022768 0.172481 0.324897 0.326504
0.024175 0.178215 0.337317 0.338611
0.029904 0.183622 0.338799 0.340016
0.016936 0.172481 0.331656 0.333060
0.023056 0.174012 0.332869 0.333940

# Collecting request times to server running in Local Zone NYC
# [endpoint]
time_namelookup time_connect time_starttransfer time_total
0.023081 0.182175 0.339818 0.340769
0.022908 0.187502 0.353711 0.354144
0.024140 0.181430 0.342511 0.343637
0.016736 0.175075 0.337191 0.338269
0.017669 0.180589 0.342865 0.343365

# Collecting request times to server running in Local Zone BUE
# [endpoint]
time_namelookup time_connect time_starttransfer time_total
0.000016 0.044052 0.090594 0.091382
0.000018 0.043565 0.090848 0.091869
0.000015 0.046529 0.092182 0.092997
0.000019 0.043899 0.089382 0.090326
0.000016 0.044163 0.089726 0.090368


  • Aggregated results:

Figure 5: Average connect and total time with slower points based on the baseline (fastest)

The total time to connect, in milliseconds, from the client in NYC (outside AWS) to the OpenShift edge node running in the Local Zone was ~66% lower than the application running in the regular zones. It's also worth mentioning that there are benefits when clients access from different countries: the results from the client in Brazil decreased by more than 100% of the total request time when accessing the Buenos Aires deployment, instead of going to the Region's app due to the geographic proximity of those locations.


Figure 6: Client in NYC getting better experience accessing the NYC Local Zone, than the app in the region


OpenShift provides a platform for easy deployment, scaling, and management of containerized applications across the hybrid cloud including AWS. Using OpenShift with AWS Local Zones provides numerous benefits for organizations. It allows for lower latency and improved network performance as Local Zones are physically closer to end users, which enhances the overall user experience and reduces downtime. The combination of OpenShift and AWS Local Zones provides a flexible and scalable solution that enables organizations to modernize their applications and meet the demands of their customers and users:

  1. Improving application performance and user experience,
  2. Hosting resources in specific geographic locations reducing overall cost and
  3. Providing regulated industries with a way to meet data residency requirements.

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