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RH OVE Ecosystem Sizing Plan

Executive Summary

This document outlines the sizing estimates for the RH OVE Ecosystem implementation. It addresses the expected capacity and complexity for each sub-project. The plan ensures that proper resource allocation and infrastructure setups are achieved to meet performance and scalability demands.

** TO BE REVIEW FOR NODE SIZING **

Infrastructure Sizing

Scope: Support up to 200 hybrid applications (containerized, PaaS, and VMs) and 2,000 virtual machines.

Key Metrics

  • Application Types: Hybrid, combining containers, PaaS services, and virtual machines.
  • Maximum Applications: 200
  • Maximum Virtual Machines: 2,000
  • Expected Compute Resources:
  • CPUs: Approximately 10,000 vCPUs
  • Memory: Approximately 20 TB RAM
  • Storage: Approximately 1 PB (Petabyte)
  • Network Capacity: High-throughput connectivity with redundant failover capabilities.
  • Security and Compliance: Adherence to regulatory standards with IAM components and network policies.

Resource Allocation

Node Size Options

To optimize resource allocation and cost efficiency, three node size configurations are proposed:

  • Node Configuration: 16 vCPUs, 64 GB RAM, 1 TB NVMe SSD
  • Number of Nodes: 625 nodes
  • Use Case: Development environments, testing workloads, small applications
  • Cost Efficiency: Lower initial investment, flexible scaling
  • Pros:
  • Lower hardware costs per node
  • Better granular scaling
  • Reduced blast radius for failures
  • Cons:
  • Higher management overhead
  • More network complexity
  • Node Configuration: 32 vCPUs, 128 GB RAM, 2 TB NVMe SSD
  • Number of Nodes: 313 nodes
  • Use Case: Production workloads, hybrid applications, medium-scale VMs
  • Cost Efficiency: Balanced performance and cost
  • Pros:
  • Optimal resource density
  • Balanced management overhead
  • Good performance isolation
  • Cons:
  • Higher individual node cost
  • Less flexible for small workloads
  • Node Configuration: 64 vCPUs, 256 GB RAM, 4 TB NVMe SSD
  • Number of Nodes: 157 nodes
  • Use Case: High-performance applications, large VMs, compute-intensive workloads
  • Cost Efficiency: Best performance per dollar for large workloads
  • Pros:
  • Maximum resource density
  • Lower management overhead
  • Best for large workloads
  • Cons:
  • Higher blast radius
  • Less flexibility for smaller workloads
  • Higher individual node investment

Distribution across clusters: - Management Cluster: 6 Medium nodes (dedicated for cluster management) - Production Clusters: - 60% Medium nodes (188 nodes) - Primary production workloads - 30% Large nodes (47 nodes) - High-performance applications - 10% Small nodes (62 nodes) - Development and testing

Total Node Count: 303 nodes Total Resources: ~10,000 vCPUs, ~20 TB RAM, ~600 TB Storage

Network and Storage Specifications

  • Network Bandwidth: Up to 40 Gbps per cluster
  • Storage IOPS: Minimum 200,000 IOPS aggregate
  • Network Architecture:
  • 25 Gbps per node connectivity
  • Redundant spine-leaf topology
  • Dedicated storage network (10 Gbps)

Application Gabari Descriptions

To ensure compatibility and optimal performance, applications are categorized based on typical resource demands and architectural patterns:

1. Microservices Applications

  • Configuration: Typically small, scalable units with minimal resource needs per instance (1-2 vCPUs, 2-4 GB RAM)
  • Key Considerations: Designed for high scalability, containerized deployments, and stateless architecture
  • Use Cases: Web services, REST APIs, lightweight backend services

2. Monolithic Applications

  • Configuration: Larger resource footprint with robust processing needs (4-8 vCPUs, 16-32 GB RAM)
  • Key Considerations: May not scale horizontally; benefits from vertical scaling
  • Use Cases: Legacy applications, computational intensive tasks, single-platform systems

3. Distributed Applications

  • Configuration: Moderate resources per service, optimized for distributed workload (2-4 vCPUs, 8-16 GB RAM per node)
  • Key Considerations: Requires synchronization across nodes, often benefits from microservices/design separation
  • Use Cases: Databases, clustered applications, interconnected services

4. Resource-Intensive Applications

  • Configuration: High-performance requirements, large scale of resources (8-16 vCPUs, 32-64 GB RAM)
  • Key Considerations: Compute-intensive, may need specific hardware accelerators (e.g., GPUs)
  • Use Cases: Data analytics, machine learning workloads, scientific computing

Migration Sizing

Scope: Plan for the migration of 1,000 virtual machines and 100 applications.

Key Metrics

  • Virtual Machines: 1,000
  • VM Types: Includes various OS types and legacy configurations
  • Average VM size: 4 vCPUs, 16 GB RAM per VM
  • Storage per VM: 500 GB
  • Applications: 100
  • Application Types: Legacy, modern monoliths, and distributed services
  • Data Migration Volume: 500 TB

Migration Planning

  • Migration Waves: 5 waves, 200 VMs + 20 Applications per wave
  • Expected Downtime: Max 2 hours per application
  • Risk Mitigation:
  • Pilot migrations for high-risk workloads
  • Rollback strategies for failed migrations

Strategic Considerations

Infrastructure

  1. Scalability: Design to accommodate future growth up to 300 applications and 3,000 VMs.
  2. Redundancy: Implement failover and disaster recovery protocols.
  3. Monitoring and Logging: Comprehensive observability with real-time analytics.

Migration

  1. Compatibility: Analyze application dependencies and compatibility early.
  2. Data Integrity: Ensure lossless data transfer methods.
  3. Operational Support: Equip teams with runbooks for migration phases.

Appendices

Sizing Assumptions

  • Based on existing organizational usage patterns and vendor best practices.

Dependencies

  • Align sizing with strategic initiatives.
  • Regular reviews to anticipate scaling needs and compliance demands.

Risk Factors

  • Sizing models subject to change with evolving requirements and emerging technologies.