Multi-tenant Self-service Clusters - Rafay Product Documentation

Multi-Tenant Self-Service Clusters

What is it?

What are the Issues?

Why is it a problem?

Proposed Implementation Framework

  1. Implement Multi-tenancy Architecture

    • Develop a robust multi-tenancy model using Kubernetes namespaces, network policies, and resource quotas.
    • Create isolation mechanisms to ensure workloads from different tenants cannot interfere with each other.
    • Implement hierarchical namespace management to support organizational structures and team hierarchies.
    • Develop automated processes for tenant onboarding and offboarding, including resource allocation and cleanup.
  2. Establish Self-service Provisioning Portal

    • Create a user-friendly self-service portal for application teams to request and manage their cluster resources.
    • Implement role-based access control (RBAC) to ensure teams can only access and manage their own resources.
    • Develop automated workflows for resource requests, approvals, and provisioning.
    • Create customizable templates for common application patterns to accelerate deployment.
  3. Implement Resource Optimization and Governance

    • Develop automated resource allocation and scaling mechanisms based on actual usage and predefined policies.
    • Implement cost allocation and chargeback systems to provide visibility into resource consumption by each tenant.
    • Create centralized policy management to enforce security, compliance, and operational standards across all tenants.
    • Develop monitoring and alerting systems to identify and address resource contention or policy violations.
  4. Enable Advanced Observability and Management

    • Implement comprehensive monitoring and logging solutions that provide both cluster-wide and tenant-specific views.
    • Develop customizable dashboards for different stakeholders (e.g., cluster admins, tenant admins, developers).
    • Create automated reporting mechanisms for resource utilization, performance, and compliance status.
    • Implement predictive analytics and capacity planning tools to optimize resource allocation and cluster scaling.