Multi-Tenancy in K8s & OpenShift #9 - Strengthening Control Plane Security

In the previous blog, we discussed Ingress Control Isolation for External Access Segregation, emphasizing the importance of managing external access to each tenant’s applications while ensuring security and isolation. Throughout this series, we’ve also covered key topics such as Namespace-Based Isolation, Network Policies, RBAC, Resource Quotas and LimitRanges, Pod Security Standards (PSS), and Storage Isolation to ensure secure and fair resource and data management in multi-tenant Kubernetes clusters.

In Kubernetes, the control plane is the central component responsible for managing the cluster’s overall operations. It includes essential services like the API server, scheduler, and controller manager, which handle tasks such as scheduling pods, managing nodes, and responding to requests. In a multi-tenant environment, where multiple tenants share the same control plane, ensuring its robustness is critical to maintaining stability and preventing any one tenant from overloading or disrupting the cluster.

Multi-Tenancy in Kubernetes & Openshift: A Comprehensive Guide

Part 1: Use Cases & Implementations

Part 2: Namespace-Based Isolation for Workload Separation

Part 3: Network Policies for Network Isolation

Part 4: Role-Based Access Control (RBAC) for Authorization

Part 5: Resource Quotas and LimitRanges for Resource Control

Part 6: Pod Security Standards (PSS) for Workload Security

Part 7: Storage Isolation for Persistent Volume Security

Part 8: Ingress Control Isolation for External Access Segregation

Part 9: Control Plane Robustness to Safeguard shared Kubernetes Resources

Part 10: NodePort and HostPort Restrictions for Enhanced Network Security

Part 11: Resource and Cost Tracking for ShowBack/ChargeBack

Part 12: Multi-Tenant Considerations for Shared Tools

Control Plane Robustness to Safeguard shared Kubernetes Resources

Why Control Plane Robustness Is Important in Multi-Tenancy

In multi-tenant Kubernetes clusters, all tenants share the same control plane, which can lead to potential issues if one or more tenants:

• Generate excessive requests: A misconfigured or malicious tenant might flood the API server with requests, exhausting resources and affecting other tenants.

• Cause denial-of-service (DoS) risks: Excessive API calls can slow down the control plane, making it unresponsive to other tenants’ requests.

• Consume resources unpredictably: Without controls, tenants could unpredictably consume control plane resources, leading to degraded performance for everyone.

To prevent these issues, implementing control plane robustness measures such as rate limiting and event control helps safeguard shared resources, ensuring all tenants receive fair access and the control plane remains stable.

How to Achieve Control Plane Robustness

• Event Rate Limiting: Use the EventRateLimit admission controller to limit the number of API requests and events a tenant can generate within a certain period. This helps mitigate the risk of one tenant overloading the API server.

• API Request Quotas: Configure request quotas to limit the frequency of API calls per tenant, preventing excessive usage and ensuring fair resource distribution. This is especially useful in clusters with high API demand, like those with many automated deployments or frequent scaling events.

• Admission Controllers: Use admission controllers to inspect and control requests to the API server. The EventRateLimit admission controller, for example, can enforce API rate limits based on the namespace, user, or ServiceAccount.

• Cluster Autoscaler Controls: Configure the Cluster Autoscaler (if enabled) with tenant-specific settings to prevent overloading cluster resources. Make sure autoscaling policies align with quotas, so tenants don’t inadvertently consume excessive resources.

Use Cases for Control Plane Robustness in Multi-Tenancy

• Throttling High-Volume Requests: Tenants with automation scripts, CI/CD pipelines, or monitoring tools might generate high volumes of API requests. Rate limiting helps prevent these tenants from overwhelming the API server.

• Preventing Denial-of-Service Scenarios: In a multi-tenant environment, a single tenant could unintentionally cause a DoS scenario by generating too many requests. By using rate limiting and API quotas, we can ensure that one tenant’s activity doesn’t impact the stability of the entire cluster.

• Ensuring Fair Resource Distribution: Limiting request rates per tenant ensures all tenants get a fair share of control plane resources, preventing “noisy neighbors” from monopolizing the API server.

Best Practices for Control Plane Robustness

1. Define Reasonable API Limits: Set realistic rate limits for tenants based on expected usage patterns. For instance, tenants with large-scale deployments might need higher quotas, while others may require only minimal API access.

2. Monitor and Adjust Rate Limits Regularly: Track control plane metrics to identify excessive usage patterns and adjust rate limits as needed. Monitoring tools like Prometheus and Grafana provide valuable insights into API usage and control plane performance.

3. Use Namespace-Specific Policies: Tailor rate limits to specific namespaces, ensuring tenants with higher demands have appropriate quotas while restricting others. This prevents overuse without unnecessarily limiting tenants with legitimate needs.

4. Audit and Log API Requests: Enable API server audit logs to track request patterns and detect tenants violating rate limits or attempting unauthorized access. Regular audits help identify potential security issues and optimize rate-limiting policies.

Conclusion

Control plane robustness is essential in multi-tenant Kubernetes environments to ensure fair access to shared resources and maintain cluster stability. By implementing rate limiting, request quotas, and admission controls, we can safeguard the control plane from overuse and protect it from risks associated with excessive tenant activity. These practices help maintain a stable and reliable multi-tenant Kubernetes cluster, allowing tenants to share infrastructure without compromising performance or security.

Simplifying Multi-Tenancy with Stakater Multi-Tenant Operator

Setting up multi-tenancy in Kubernetes can be a complex task, requiring in-depth Kubernetes knowledge and careful configuration of namespace isolation, network policies, RBAC, resource quotas, and more. Ensuring robust security and resource management is time-consuming but essential. This is where the Stakater Multi-Tenant Operator (MTO) comes in.

Designed to simplify and accelerate multi-tenancy on Kubernetes, the Stakater MTO provides a robust, automated framework for managing tenants. It helps organizations quickly establish secure, isolated, and well-managed environments across shared clusters. The MTO also addresses control plane robustness—crucial for a stable multi-tenant environment—by supporting rate limiting, request quotas, and other controls. These capabilities safeguard the shared control plane, ensuring fair resource distribution and stability even in high-demand scenarios.

In our next blog, we’ll explore NodePort and HostPort Restrictions for Enhanced Network Security, focusing on the importance of controlling service exposure in multi-tenant setups to further enhance security and minimize risks.