Simplifying Envoy Gateway Proxy Configuration for On-Premises RKE2 Clusters Without LoadBalancer Services

Introduction

Deploying an Envoy Gateway proxy in on-premises Kubernetes (RKE2) clusters often encounters challenges due to the default reliance on LoadBalancer services. While effective in cloud environments, these services necessitate additional components such as MetalLB or ServiceLB in on-premises setups. These dependencies introduce operational complexity, potential single points of failure, and increased maintenance requirements. The underlying issue stems from LoadBalancer services' dependence on external infrastructure—such as cloud provider-managed load balancers—which on-premises environments inherently lack. Without these components, traffic routing fails, rendering the deployment non-functional.

To address this, we propose a configuration that eliminates LoadBalancer services entirely while ensuring seamless integration with existing infrastructure. This approach focuses on preserving client source IP addresses, preventing port conflicts in multi-gateway deployments, and minimizing network latency. The solution leverages a DaemonSet and host ports, effectively bypassing the need for external load balancers and reducing operational overhead.

Key Mechanisms

• DaemonSet Deployment: An Envoy proxy pod is deployed on every worker node, ensuring traffic is processed locally. This architecture eliminates the need for a central LoadBalancer service, as each pod binds directly to host ports 80 and 443. The causal relationship is clear: local traffic processing → reduced network hops → enhanced performance and latency.
• MergeGateways: Enabling mergeGateways: true consolidates multiple gateways into a single Envoy proxy instance. This prevents port conflicts that occur when multiple gateways attempt to bind to the same ports. The mechanism is straightforward: single instance → shared port bindings → conflict elimination.
• ClusterIP Service: A ClusterIP service is created to facilitate Kubernetes service discovery, though it is not used for external traffic routing. Instead, Envoy pods handle external traffic directly via host ports, bypassing the service entirely. The mechanism ensures: ClusterIP service → internal discovery → direct external traffic handling by Envoy pods.
• Client IP Preservation: Setting externalTrafficPolicy: Local routes traffic to the Envoy pod on the same node as the client, preserving source IP addresses. This avoids Network Address Translation (NAT) and ensures: local routing → no NAT → intact client IPs.

Example Configuration

The following YAML configuration implements the described mechanisms:

apiVersion: gateway.envoyproxy.io/v1alpha1kind: EnvoyProxymetadata: name: eg-example namespace: envoy-gateway-systemspec: mergeGateways: true provider: type: Kubernetes kubernetes: useListenerPortAsContainerPort: true envoyDaemonSet: patch: type: Strategic value: spec: template: spec: containers: - name: envoy ports: - containerPort: 80 hostPort: 80 - containerPort: 443 hostPort: 443 envoyService: type: ClusterIP externalTrafficPolicy: Local

Edge Cases and Trade-offs

While this configuration significantly simplifies deployment, it introduces specific trade-offs:

• Node Failure Risk: If a worker node fails, traffic directed to its Envoy pod is lost. Mitigation strategies include employing external load balancers or DNS round-robin to distribute traffic across nodes. Mechanism: node failure → traffic loss → external load distribution.
• Resource Consumption: Deploying an Envoy pod on every node increases resource utilization. Mechanism: additional pods → higher CPU and memory consumption.
• Limited Scalability: This approach is optimal for small to medium-sized clusters. Larger clusters may require more advanced load-balancing solutions to handle increased traffic. Mechanism: cluster growth → higher traffic volume → potential performance bottlenecks.

Conclusion

This streamlined Envoy Gateway configuration effectively eliminates the complexities associated with LoadBalancer services in on-premises RKE2 clusters. By leveraging a DaemonSet, host ports, and gateway merging, it ensures seamless integration with existing infrastructure while preserving client IPs and minimizing network latency. Although not universally applicable, this approach offers a practical solution for organizations seeking to reduce operational overhead and enhance reliability in their Kubernetes deployments.

Understanding the Problem: LoadBalancer Services in On-Premises Kubernetes

Deploying Envoy Gateway in on-premises Kubernetes clusters, particularly RKE2, often relies on LoadBalancer services, which introduce significant operational challenges. In cloud environments, LoadBalancer services are abstracted and managed by cloud providers, but on-premises setups lack this integration. To replicate this functionality, organizations must deploy additional components such as MetalLB or ServiceLB. These solutions, while functional, increase operational complexity and introduce potential single points of failure. For example, MetalLB requires manual configuration of IP address pools and BGP peering, which can lead to misconfigurations, network instability, or traffic blackholing if not meticulously managed.

Key Challenges with LoadBalancer Services in On-Premises Environments

• Vendor Lock-In: LoadBalancer add-ons often bind organizations to specific vendors or configurations, limiting flexibility and increasing long-term costs.
• Operational Overhead: Managing additional components demands dedicated resources and specialized expertise, exacerbating maintenance burdens.
• Single Points of Failure: Each added component introduces failure vectors, such as misconfigured IP pools or BGP sessions, which can disrupt traffic flow.

Root Causes of LoadBalancer Complexity

These challenges stem from the fundamental mismatch between cloud-native LoadBalancer services and on-premises infrastructure. In cloud environments, LoadBalancer services abstract IP allocation, routing, and scaling, tasks handled by the provider. On-premises, these responsibilities fall on the operator, requiring manual intervention. Key failure points include:

1. IP Allocation: MetalLB’s reliance on predefined IP pools can lead to exhaustion or misconfiguration, preventing service provisioning and causing deployment failures.
2. BGP Peering: MetalLB uses BGP to advertise IPs, but incorrect configurations can result in traffic blackholing, where packets are silently dropped due to routing errors.
3. ServiceLB Dependencies: ServiceLB’s dependence on external load balancers may introduce compatibility issues or require additional licensing, further complicating deployment.

The Need for a Streamlined Solution

To address these challenges, a streamlined Envoy Gateway configuration is proposed, eliminating LoadBalancer services entirely. This approach leverages existing infrastructure while minimizing operational overhead. The solution, as detailed in the source case, achieves this through:

• ClusterIP Service Replacement: A ClusterIP service replaces the LoadBalancer service, eliminating the need for external add-ons and reducing complexity.
• DaemonSet Deployment: An Envoy proxy DaemonSet is deployed on every worker node, binding directly to host ports 80 and 443. This localizes traffic handling, bypassing the need for an external load balancer.
• Gateway Consolidation: Setting mergeGateways: true merges multiple gateways into a single Envoy instance, preventing port conflicts inherent in multi-gateway deployments.

Edge Cases and Trade-Offs

While this configuration simplifies deployment, it introduces specific trade-offs:

Edge Case	Mechanism	Observable Effect
Node Failure	Failure of a worker node renders the Envoy pod on that node unavailable, preventing traffic routing through it.	Traffic loss for clients assigned to the failed node. Mitigation: Employ external load balancers or DNS round-robin for traffic distribution.
Resource Consumption	Deploying an Envoy pod on every node increases cluster-wide CPU and memory usage.	Higher resource utilization, particularly in resource-constrained environments.
Scalability Limits	The configuration is optimized for small to medium-sized clusters. Larger clusters may exceed traffic handling and resource capacity.	Potential performance degradation in large clusters due to increased resource consumption and traffic handling limitations.

By understanding these trade-offs and their underlying mechanisms, organizations can confidently adopt this streamlined Envoy Gateway configuration for on-premises RKE2 clusters, balancing simplicity with operational constraints.

Streamlining Envoy Gateway Deployment in On-Premises RKE2 Clusters

Deploying Envoy Gateway in on-premises Kubernetes (RKE2) environments often encounters challenges with LoadBalancer services, which are inherently designed for cloud infrastructures. The conventional approach necessitates the introduction of add-ons like MetalLB or ServiceLB, which, while functional, increase complexity, introduce potential single points of failure, and elevate maintenance overhead. This article presents six streamlined Envoy Gateway proxy configurations tailored for on-premises RKE2 clusters, each designed to eliminate LoadBalancer dependencies and leverage existing infrastructure. By dissecting the mechanics, trade-offs, and suitability of each approach, we provide a practical framework for simplifying deployment and reducing operational complexity.

1. DaemonSet with Host Ports and MergeGateways

This configuration eliminates LoadBalancer services by deploying an Envoy proxy DaemonSet across all worker nodes. Each Envoy pod binds directly to host ports 80 and 443, bypassing the need for external load balancers. The mergeGateways: true setting consolidates multiple gateways into a single Envoy instance, preventing port conflicts.

• Mechanism: By binding directly to host ports, Envoy pods process traffic locally on each node, minimizing network hops. The DaemonSet ensures an Envoy instance runs on every worker node, while mergeGateways avoids port collisions by sharing bindings across gateways. This configuration preserves client source IPs via externalTrafficPolicy: Local.
• Advantages: Simplifies deployment, reduces operational overhead, and maintains client IP visibility.
• Trade-offs: Node failures result in traffic loss for clients assigned to that node. Increased resource consumption due to Envoy pods running on every node.
• Use Case: Small to medium-sized clusters with existing external load balancers or DNS round-robin setups.

2. ClusterIP Service with External Load Balancer Integration

This approach replaces LoadBalancer services with a ClusterIP service, relying on an external load balancer to distribute traffic to worker nodes. Envoy pods bind to host ports, and the external load balancer ensures traffic is evenly distributed across nodes.

• Mechanism: The ClusterIP service facilitates internal Kubernetes service discovery, while the external load balancer handles external traffic distribution. Envoy pods process traffic locally, preserving client IPs via externalTrafficPolicy: Local.
• Advantages: Mitigates node failure risks by distributing traffic across nodes. Suitable for clusters with existing load-balancing infrastructure.
• Trade-offs: Introduces an external dependency, increasing complexity. Requires configuration and compatibility with the external load balancer.
• Use Case: Clusters with existing load-balancing solutions seeking to minimize Envoy Gateway complexity.

3. NodePort Service with DNS Round-Robin

This configuration uses a NodePort service to expose Envoy pods on a static port across all nodes. DNS round-robin distributes traffic across worker nodes without requiring an external load balancer.

• Mechanism: The NodePort service assigns a static port to Envoy pods on each node. DNS round-robin rotates client requests across node IPs, spreading traffic. Client IPs are preserved via externalTrafficPolicy: Local.
• Advantages: Avoids external load balancers and maintains client IP visibility. Simple to implement with basic DNS configuration.
• Trade-offs: Uneven traffic distribution due to DNS caching. Node failures impact clients assigned to that node.
• Use Case: Small clusters with minimal traffic and no existing load-balancing infrastructure.

4. Optimized MetalLB with BGP Peering

This scenario retains MetalLB but optimizes its configuration to minimize risks. BGP peering is used to advertise IP addresses, and IP pools are carefully managed to prevent exhaustion.

• Mechanism: MetalLB assigns IPs from a predefined pool and uses BGP to advertise these IPs to the network. Envoy pods are exposed via LoadBalancer services, ensuring efficient traffic routing.
• Advantages: Leverages existing MetalLB setup. BGP ensures efficient and reliable traffic routing.
• Trade-offs: Requires meticulous IP pool management to avoid exhaustion. BGP misconfigurations can lead to traffic blackholing.
• Use Case: Organizations already using MetalLB seeking to optimize its configuration.

5. ServiceLB with External Load Balancer Integration

This approach integrates ServiceLB with an external load balancer, combining Kubernetes service discovery with external traffic distribution. Envoy pods are exposed via LoadBalancer services managed by ServiceLB.

• Mechanism: ServiceLB provisions LoadBalancer services, and the external load balancer routes traffic to Envoy pods. Client IPs are preserved via externalTrafficPolicy: Local.
• Advantages: Combines Kubernetes service discovery with external load balancing. Suitable for hybrid cloud environments.
• Trade-offs: Increases complexity with multiple components. Requires compatibility between ServiceLB and the external load balancer.
• Use Case: Hybrid cloud setups needing seamless integration between on-prem and cloud environments.

6. Ingress-Nginx Hybrid with Envoy Gateway

This configuration combines Ingress-Nginx with Envoy Gateway, using Ingress-Nginx for basic routing and Envoy Gateway for advanced traffic management. Envoy pods are deployed via a DaemonSet, and Ingress-Nginx handles external traffic distribution.

• Mechanism: Ingress-Nginx acts as the entry point, routing traffic to Envoy pods based on defined rules. Envoy handles advanced traffic management, such as rate limiting and circuit breaking.
• Advantages: Leverages Ingress-Nginx’s simplicity for basic routing. Envoy Gateway provides advanced features without the complexity of a full Envoy deployment.
• Trade-offs: Introduces an additional component, increasing operational overhead. Requires coordination between Ingress-Nginx and Envoy Gateway.
• Use Case: Clusters needing both basic and advanced traffic management capabilities.

Critical Trade-Offs and Edge Cases

Each configuration introduces trade-offs that must be carefully evaluated to align with specific infrastructure and operational requirements:

• Node Failure Resilience: DaemonSet-based setups risk traffic loss during node failures. Mitigation strategies include external load balancers or DNS round-robin.
• Resource Consumption: Deploying Envoy pods on every node increases CPU and memory usage, which may impact performance in resource-constrained environments.
• Scalability: DaemonSet-based configurations are optimal for small to medium clusters. Larger clusters may experience performance degradation due to increased resource demands.

Conclusion

Selecting the appropriate Envoy Gateway configuration for on-premises RKE2 clusters requires a nuanced understanding of infrastructure, operational constraints, and traffic patterns. The DaemonSet with Host Ports and MergeGateways approach offers unparalleled simplicity and reduced operational overhead but introduces node failure risks. Hybrid solutions, such as optimized MetalLB or Ingress-Nginx integration, provide flexibility at the cost of increased complexity. By rigorously evaluating the mechanics and trade-offs of each scenario, organizations can confidently adopt a configuration that balances simplicity, reliability, and scalability, ensuring optimal performance in their specific environment.

Implementation and Best Practices

Deploying Envoy Gateway in on-premises Kubernetes clusters (specifically RKE2) without relying on LoadBalancer services requires a strategic approach to minimize complexity and maximize efficiency. This configuration leverages existing infrastructure, reduces operational overhead, and eliminates the need for external load balancers. Below is a detailed, step-by-step guide, including YAML examples, resource optimization strategies, and security best practices.

Step-by-Step Implementation

The core of this configuration involves deploying Envoy Gateway as a DaemonSet, binding it to host ports, and merging multiple gateways into a single Envoy instance. This approach ensures efficient traffic handling and reduces network latency.

1. Configure EnvoyProxy Resource

Define the EnvoyProxy resource to disable the default LoadBalancer service and enable gateway merging. This configuration ensures Envoy pods listen directly on host ports 80 and 443, bypassing the need for external load balancers.

YAML Example:apiVersion: gateway.envoyproxy.io/v1alpha1kind: EnvoyProxymetadata: name: eg-example namespace: envoy-gateway-systemspec: mergeGateways: true Merge all gateways into a single Envoy instance provider: type: Kubernetes kubernetes: useListenerPortAsContainerPort: true Bind container ports to host ports envoyDaemonSet: patch: type: Strategic value: spec: template: spec: containers: - name: envoy ports: - containerPort: 80 hostPort: 80 Bind to host port 80 - containerPort: 443 hostPort: 443 Bind to host port 443 envoyService: type: ClusterIP Use ClusterIP instead of LoadBalancer externalTrafficPolicy: Local Preserve client IPs by routing traffic locally

Mechanism: By setting mergeGateways: true, multiple gateways share a single Envoy instance, eliminating port conflicts. Binding to host ports allows Envoy pods to handle traffic directly on each worker node, removing the dependency on external load balancers.

2. Deploy Envoy Gateway

Apply the configuration to your RKE2 cluster using the following command. Once deployed, Envoy pods will run on every worker node, listening on ports 80 and 443.

Command:kubectl apply -f envoyproxy.yaml

Mechanism: The DaemonSet ensures an Envoy pod runs on each worker node, processing traffic locally. This architecture minimizes network hops and reduces latency by handling traffic directly on the node receiving it.

3. Configure External Traffic Routing

With Envoy pods listening on host ports, external traffic can be routed using existing infrastructure, such as external load balancers or DNS round-robin. For example, configure DNS to point to the worker node IPs.

Mechanism: DNS round-robin distributes requests evenly across worker nodes, ensuring balanced traffic distribution. In the event of node failure, traffic is automatically redirected to healthy nodes, minimizing downtime.

Resource Optimization Tips

• Limit Resource Requests and Limits: Define CPU and memory requests/limits for Envoy pods to prevent resource contention and ensure stable performance.

  resources: requests: cpu: "500m" memory: "512Mi" limits: cpu: "1000m" memory: "1Gi"

• Monitor Resource Usage: Utilize monitoring tools like Prometheus and Grafana to track Envoy pod resource consumption. Adjust limits proactively to maintain optimal performance.
• Optimize Gateway Configuration: Disable unused Envoy features (e.g., filters or listeners) to reduce memory footprint and improve efficiency.

Security and Performance Best Practices

• Enable TLS Termination: Configure Envoy to terminate TLS on port 443, ensuring encrypted communication. Use cert-manager for automated certificate management.

  Example Listener Configuration:listeners: - address: socketAddress: address: 0.0.0.0 portValue: 443 filterChains: - filters: - name: envoy.filters.network.http_connection_manager typedConfig: ... httpFilters: - name: envoy.filters.http.router

• Implement Network Policies: Use Kubernetes Network Policies to restrict access to Envoy pods, allowing only authorized traffic.
• Regularly Update Envoy Gateway: Stay current with the latest Envoy Gateway releases to leverage security patches and performance enhancements.

Edge Case Analysis

1. Node Failure Risk

Mechanism: If a worker node fails, traffic routed to that node is lost, as Envoy pods are bound to host ports without automatic cluster-level failover.

Mitigation: Implement external load balancers or DNS round-robin to distribute traffic across nodes. External load balancers can detect node failures and redirect traffic, while DNS round-robin ensures even request distribution.

2. Resource Consumption

Mechanism: Running Envoy pods on every worker node increases CPU and memory usage, which may impact resource-constrained environments.

Mitigation: Optimize Envoy resource requests/limits and disable unused features. Continuously monitor resource usage to identify and address bottlenecks.

3. Scalability Limits

Mechanism: DaemonSet-based deployments are optimal for small to medium clusters. Larger clusters may experience performance degradation due to increased resource consumption and network overhead.

Mitigation: For larger clusters, consider advanced load-balancing solutions or hybrid configurations that combine Envoy Gateway with external load balancers.

Conclusion

This streamlined Envoy Gateway configuration eliminates the complexity of LoadBalancer services in on-premises RKE2 clusters by leveraging existing infrastructure and reducing operational overhead. By deploying Envoy as a DaemonSet, merging gateways, and binding to host ports, organizations can achieve a robust and efficient gateway solution. However, it is essential to evaluate edge cases, such as node failure risks and resource consumption, to ensure the configuration aligns with the specific needs of your cluster. This approach provides a practical, scalable, and secure foundation for integrating Envoy Gateway into on-premises Kubernetes environments.

Case Studies and Real-World Examples

Streamlining Envoy Gateway Deployment in On-Premises RKE2 Clusters: A Practical Implementation

Deploying Envoy Gateway in on-premises Kubernetes (RKE2) clusters often introduces unnecessary complexity, particularly when relying on LoadBalancer services and add-ons like MetalLB or ServiceLB. These solutions, while functional, increase operational overhead, introduce single points of failure, and necessitate meticulous configuration. We present a real-world implementation that simplifies deployment by eliminating LoadBalancer services and leveraging existing infrastructure, thereby reducing complexity and enhancing reliability.

Case Study: Envoy Gateway with DaemonSet and Host Ports

A mid-sized organization adopted Envoy Gateway for their on-premises RKE2 cluster to minimize operational complexity while ensuring robust traffic routing. The implemented solution replicates the behavior of ingress-nginx using Envoy Gateway, eliminating the need for external load balancers or additional add-ons. This approach directly addresses the challenges associated with traditional LoadBalancer-based deployments.

Key Features of the Configuration:

• Elimination of LoadBalancer Services: By utilizing a ClusterIP service instead of a LoadBalancer service, the configuration avoids dependencies on MetalLB or ServiceLB. This approach reduces potential points of failure and simplifies management by removing the need for external load balancer provisioning.
• DaemonSet Deployment: An Envoy proxy DaemonSet is deployed across all worker nodes, ensuring each node hosts a local Envoy instance. This setup binds directly to host ports 80 and 443, bypassing the need for external load balancers and enabling localized traffic handling.
• Gateway Consolidation: The mergeGateways: true setting consolidates multiple gateways into a single Envoy instance, preventing port conflicts and optimizing resource utilization by reducing the number of running instances.

Technical Mechanism:

The Envoy proxy DaemonSet binds to host ports 80 and 443 on each worker node, enabling local traffic processing. The externalTrafficPolicy: Local setting ensures client IP preservation by routing traffic directly to the Envoy pod on the node it reaches. This localized processing minimizes network hops, reduces latency, and improves overall performance.

For instance, when a request arrives at a worker node, the Envoy pod listens on host port 80 or 443, processes the request, and routes it to the appropriate backend service. This architecture ensures efficient traffic handling without relying on external load balancing mechanisms.

Edge Case Analysis:

• Node Failure: In the event of a node failure, clients directed to that node will experience traffic loss. This risk is mitigated by employing DNS round-robin or an external load balancer to redistribute traffic to healthy nodes, ensuring high availability.
• Resource Consumption: Running an Envoy pod on every node increases CPU and memory usage. To address this, the organization implemented resource requests and limits (e.g., requests: cpu: "500m", memory: "512Mi") and disabled unused Envoy features, optimizing resource utilization.
• Scalability Limits: This configuration is optimized for small to medium-sized clusters. Larger clusters may experience performance degradation due to increased resource consumption and network overhead. For such environments, advanced load-balancing solutions or hybrid configurations are recommended to maintain performance.

Measurable Outcomes:

• Reduced Complexity: Eliminating LoadBalancer services and add-ons significantly reduced operational overhead and potential points of failure, streamlining cluster management.
• Improved Performance: Localized traffic processing on worker nodes minimized network latency, resulting in faster response times and enhanced user experience.
• Cost Savings: Avoiding external load balancers and add-ons reduced infrastructure and licensing costs, providing a cost-effective solution.

Configuration Example:

apiVersion: gateway.envoyproxy.io/v1alpha1kind: EnvoyProxymetadata: name: eg-example namespace: envoy-gateway-systemspec: mergeGateways: true provider: type: Kubernetes kubernetes: useListenerPortAsContainerPort: true envoyDaemonSet: patch: type: Strategic value: spec: template: spec: containers: - name: envoy ports: - containerPort: 80 hostPort: 80 - containerPort: 443 hostPort: 443 envoyService: type: ClusterIP externalTrafficPolicy: Local

Lessons Learned:

This implementation demonstrates that a streamlined Envoy Gateway configuration can significantly reduce complexity in on-premises RKE2 clusters. By leveraging DaemonSets, host ports, and gateway consolidation, organizations can achieve efficient traffic routing without relying on LoadBalancer services or additional add-ons. However, careful consideration of edge cases, such as node failure and resource consumption, is critical to ensuring reliability and performance.

For organizations with similar infrastructure and operational constraints, this approach offers a practical, scalable solution that balances simplicity with functionality, making it an ideal choice for modern Kubernetes deployments.

Conclusion and Next Steps

Deploying Envoy Gateway in on-premises RKE2 clusters without LoadBalancer services fundamentally simplifies the architecture by eliminating dependencies on external load-balancing mechanisms. This approach leverages a DaemonSet deployment with host ports and consolidates multiple gateways into a single Envoy instance, directly addressing the challenges of complex service exposure and resource inefficiency. By avoiding add-ons like MetalLB or ServiceLB, this configuration reduces operational overhead and minimizes potential failure points. Below is a structured summary of key insights and actionable steps for implementation:

Key Takeaways

• Elimination of External Dependencies: Bypassing LoadBalancer services removes the need for third-party load-balancing solutions, reducing configuration complexity and eliminating single points of failure inherent in external components.
• Localized Traffic Handling: Envoy pods bind directly to host ports (80/443) on each worker node, enabling local traffic processing. This preserves client source IP addresses, reduces network hops, and lowers latency by avoiding unnecessary packet forwarding.
• Resource Consolidation: Merging multiple gateways into a single Envoy instance prevents port conflicts and optimizes resource allocation. While this increases per-node CPU and memory consumption, it enhances overall efficiency by reducing redundant processes.
• Robust Failure Handling: Node failures are mitigated through external load balancers or DNS round-robin mechanisms, ensuring traffic redistribution. Resource constraints are proactively managed by setting explicit resource limits and disabling unused Envoy features.

Next Steps

To implement this configuration, follow these structured steps:

1. Deploy the Configuration: Apply the provided EnvoyProxy YAML manifest using kubectl apply -f envoyproxy.yaml to instantiate the Envoy Gateway with the optimized settings.
2. Optimize Resource Allocation: Define precise CPU and memory requests and limits for Envoy pods (e.g., requests: cpu: "500m", memory: "512Mi"). Monitor resource utilization using Prometheus and Grafana to ensure performance and scalability.
3. Secure the Deployment: Enable TLS termination on port 443 using cert-manager for encrypted traffic. Implement Kubernetes Network Policies to restrict access to Envoy pods, reducing the attack surface.
4. Validate Edge Cases: Simulate node failures and verify traffic redistribution via DNS round-robin or external load balancers. Adjust resource limits and failure handling mechanisms based on observed behavior.

Resources and Community Support

For deeper technical insights and troubleshooting, consult the following resources:

• Envoy Gateway API Documentation
• Kubernetes Service Types Explained
• Envoy Gateway Community Discussions

Adopting this streamlined Envoy Gateway configuration for on-premises RKE2 clusters delivers tangible benefits: reduced operational complexity, improved performance, and efficient utilization of existing infrastructure. Implement this approach today to modernize your Kubernetes deployments with confidence.