adoption in IBM Power and IBM Z environments ◦ The rise of Arm: cloud adoption and servers ◦ The customer need for flexibility • What about the drive for mixed architecture clusters? ◦ What are the main use cases? ◦ What are the customer benefits? ◦ Are there different ways to solve the same problem? ◦ Is this the way the industry is going? OPENSHIFT EVERYWHERE Let’s talk about multiple architectures
high performance per watt architectures o-----------------------------o • Support for Arm on Azure (UPI) • Single Node OpenShift on Arm (bare metal) Multi-architecture Cluster • Allow more flexibility in a cluster, use different cloud platforms o------------------------------o • Multi-architecture compute platforms: ◦ AWS Arm support (GA) ◦ Azure Arm support (GA) ◦ Bare Metal Arm (TP) • Multi-architecture compute migration and upgrade support • Hosted Control Plane: ◦ AWS Arm control plane IBM Power and zSystems • Run OpenShift on highly available, highly secure, scalable hardware o-----------------------------o • FIPS Mode supported • Cluster Resource Override Operator • Network Bound Disk Encryption • Metal LB support • Egress IP support PM: Duncan Hardie Systems Enablement - latest highlights
• Performance per Watt • Cloud adoption with competitive price/performance • Edge adoption • Server support • Rapidly growing ecosystem • And finally … the same OpenShift experience you are used to today OPENSHIFT EVERYWHERE
Bare Metal Pre-existing Infrastructure (UPI) Bare Metal Layered Product Support OpenShift GitOps Red Hat Advanced Cluster Manager Logging OpenShift Pipelines
nodes Control plane Compute nodes Legacy Applications Cost Optimization Architecture Migration • Deal with that pesky app that you don’t have the source code for and is too expensive to re-write • Handle gaps in the new architecture ecosystem • Use a different architecture control plane that is made up of “cheaper” systems • Interesting to cloud providers and also IBM zSystem and IBM Power • Easy way to try out specific apps/services on a different architecture while maintaining your environment • Gradual roll out of change to another architecture not requiring “big bang” change everything at once approach • Interesting benchmark opportunity Control plane Compute nodes
nodes x86 Arm Control plane Compute nodes Control plane Compute nodes 1 Create/Install your OpenShift Cluster as normal 2 Add in different architecture nodes as a day 2 operation 3 Schedule workloads on your heterogeneous cluster OPENSHIFT EVERYWHERE
Control plane Compute nodes x86 Arm 1 Create/Install your OpenShift Cluster as normal 3 Configure your MachineSet (AWS) 4 Apply MachineSet and watch Arm node(s) get added x86 Arm 2 Point to multi-payload and update x86 payload Control plane Compute nodes x86 payload multi payload x86 Arm Control plane Compute nodes
GA • Installation of a OpenShift cluster using a manifest-listed release payload • Adding compute nodes of a different architecture as a day 2 operation • Automated upgrades between multi-arch releases • Migration support through oc commands to migrate from a single arch to a multi-arch release payload • Imagestreams support importing manifestlisted images and the internal registry also supports local pull through. • oc commands like `oc tag`, etc…also have support to tag/import manifestlists OPENSHIFT EVERYWHERE Bare Metal (Tech Preview)
Multi-Arch aware scheduling • Day 0 deployments with multiple architecture compute ◦ For installation all nodes need to be of a single architecture. • Disconnected installations ◦ Not tested yet but can be done with some manual steps • Autoscaling ◦ support for arch nodes based on weights (e.g “prefer” to use Arm instances) ◦ Autoscaling from zero • OpenShift builds classic (build v1) don’t support creating manifest listed images ◦ Use OpenShift Pipelines as a workaround OPENSHIFT EVERYWHERE
CP as pods) Central Management of CPs (easy operation & maintenance) Multi-arch support (e.g. x86 and Arm) Network & Trust segmentation Mixed Iaas For CP and Workers Fast cluster bootstrapping (CP as Pods) OPENSHIFT EVERYWHERE Hosted Control Planes (Control planes as pods) Hosted Control Planes and Multi-architecture compute
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