Platform Architecture

Installation: Deploy Cluster Autoscaler using the ClusterAutoscaler custom resource

Machine Set Integration: Configure autoscaler to work with machine sets and machine pools

Scaling Policies: Define minimum and maximum node counts per machine set and availability zone

Scale-up Triggers: Configure pod pending thresholds and resource requirements for scale-up decisions

Scale-down Policies: Set node utilization thresholds and grace periods for safe node removal

Priority Classes: Use priority classes to influence autoscaling decisions for critical workloads

Machine Autoscaler Resources: Create MachineAutoscaler custom resources for individual machine sets

Scaling Boundaries: Set appropriate minimum and maximum replica counts for different node types

Zone Distribution: Configure balanced scaling across multiple availability zones

Instance Types: Manage autoscaling for different instance types (general purpose, compute optimized, memory optimized)

Cost Optimization: Implement strategies for cost-effective scaling using spot instances and mixed instance types

Integration Testing: Test autoscaling behavior in development environments before production deployment

Resource Monitoring: Monitor cluster-wide resource utilization trends and capacity metrics

Capacity Forecasting: Plan infrastructure capacity based on growth projections and usage patterns

Multi-zone Planning: Design capacity distribution across availability zones for high availability

Reserved Capacity: Plan for reserved capacity for critical system workloads and burst scenarios

Cost Analysis: Analyze infrastructure costs and optimize resource allocation for cost efficiency

Performance Baselines: Establish performance baselines for capacity planning decisions

Cluster Mesh Architecture: Design service mesh architectures that span multiple OpenShift clusters

Cross-cluster Service Discovery: Implement service discovery mechanisms across cluster boundaries

Multi-cluster Ingress: Configure ingress controllers for traffic routing between clusters

Shared Load Balancing: Implement global load balancing across multiple cluster endpoints

Network Policy Coordination: Coordinate network policies across interconnected clusters

Certificate Management: Manage TLS certificates for secure inter-cluster communication

Submariner Installation: Deploy Submariner for cross-cluster network connectivity

Broker Configuration: Set up Submariner broker for cluster registration and coordination

Gateway Configuration: Configure gateway nodes for secure tunnel establishment

Service Export/Import: Manage service export and import across clusters using Submariner

Network CIDRs: Plan and manage non-overlapping network CIDRs across connected clusters

Troubleshooting: Debug cross-cluster connectivity issues and gateway health

Hub Cluster Setup: Configure Red Hat Advanced Cluster Manager hub cluster for multi-cluster management

Managed Cluster Registration: Register and manage multiple OpenShift clusters from central hub

Application Lifecycle: Deploy and manage applications across multiple clusters using ACM

Policy Management: Implement governance policies across multiple clusters

Observability: Monitor and observe multiple clusters from centralized dashboard

Cluster Lifecycle: Manage cluster provisioning, upgrades, and decommissioning at scale

Service Mesh Integration: Implement Istio or OpenShift Service Mesh across multiple clusters

Traffic Management: Configure traffic routing, load balancing, and failover across clusters

Security Policies: Implement mTLS and security policies in multi-cluster service mesh

Observability: Monitor service mesh traffic and performance across cluster boundaries

Federation: Configure service mesh federation for cross-cluster service communication

Certificate Management: Manage service mesh certificates and trust domains across clusters

Infrastructure Nodes: Scale dedicated infrastructure nodes for OpenShift platform components

Storage Scaling: Plan and implement storage capacity scaling for persistent workloads

Network Capacity: Monitor and scale network capacity including load balancers and ingress controllers

Registry Scaling: Scale container image registry for high-throughput image operations

Observability Scaling: Scale monitoring, logging, and alerting infrastructure with cluster growth

API Server Scaling: Monitor and optimize API server performance under varying loads

Scaling Automation: Implement automated scaling workflows using GitOps and CI/CD pipelines

Event-driven Scaling: Configure event-driven autoscaling based on application-specific triggers

Integration APIs: Use cluster autoscaler and platform APIs for custom scaling integrations

Monitoring Integration: Integrate autoscaling metrics with cluster monitoring and alerting systems

Cost Management: Implement automated cost management and optimization for scaling infrastructure

Compliance Automation: Ensure autoscaling activities maintain compliance with organizational policies