How to Elevate Application Performance and Availability in Azure with F5 NGINXaaS

In this article, we focus on how to optimize traffic distribution with F5 NGINXaaS.

F5 NGINXaaS for Azure, an Application Delivery Controller as a Service (ADCaaS), helps organizations deliver outstanding digital experiences using adaptive load balancing with optimized traffic management. This ADCaaS also tailors customization through broad configuration control and reduces complexity through technology consolidation for cloud-native deployments.

 

Adaptive Load Balancing 

Adaptive load balancing enables organizations to automatically distribute traffic across multiple backend services based on real-time demands. It can monitor traffic flow continuously and adjust dynamically based on response time or active health checks, ensuring consistent application connectivity.

Whether operating in a multi-cluster Kubernetes environment or scaling applications during request spikes, NGINXaaS for Azure can optimize traffic distribution while preserving smooth user experiences.

 

Active Health Checks

One of the F5 NGINXaaS’ built-on capabilities is active health checks that proactively monitor the status of your backend services. After identifying an unresponsive instance, F5 NGINXaaS reroutes traffic to healthy instances transparently to end users.

 

 

 

 

 

Advanced Traffic Management Patterns

F5 NGINXaaS for Azure supports advanced traffic management patterns out-of-the-box, empowering organizations to experiment, deploy, and test with minimal effort.

• Blue-Green and Canary Deployments

With blue-green and canary deployment strategies, F5 NGINXaaS enables organizations to gradually route requests to new application versions, allowing staged releases for validation. In case of any issues with the new version encountered, the changes can be easily rolled back to the previous working state, minimizing the risk of downtime.

• A/B Testing

F5 NGINXaaS simplifies A/B testing by routing traffic to multiple variants of your application based on user segmentation, allowing your organization gather insights, test hypotheses, and refine their offerings without impacting users.

• Circuit Breaker and Rate Limiting

F5 NGINXaaS also includes essential capabilities for maintaining stability under fluctuating traffic demands. The circuit breaker pattern prevents cascading failures by isolating services that exhibit abnormal behavior, preserving overall application health. Rate limiting ensures your infrastructure isn’t overwhelmed by excessive requests, protecting resources against malicious activities or unintentional spikes in traffic.

These advanced connectivity patterns reduce deployment risks, enhance scalability, and ensure reliable user experiences.

 

Multi-Cluster Scalability

F5 NGINXaaS for Azure provides the ability to distribute traffic seamlessly across Kubernetes pods within an Azure Kubernetes Services (AKS) cluster and across multiple clusters.

With the built-in Loadbalancer for Kubernetes feature, F5 NGINXaaS for Azure can dynamically build and maintain a list of load balancing targets (pods) in Kubernetes. It can also automatically update the F5 NGINXaaS configuration based on detected topology changes such as deployments of new pods or pod failures. This helps implement fine-grained, application-specific routing, security, and monitoring policies with detailed visibility into services running in AKS and ensure consistent user experience.

In addition, F5 NGINXaaS’ support for multi-cluster topologies enables advanced failover and disaster recovery scenarios, helping achieve business continuity.

 

How to Deploy and Configure F5 NGINXaaS

You can find F5 NGINXaaS on the Azure marketplace.

We have also curated a workshop consisting of self-paced lab exercises to help you get up and running quickly with F5 NGINXaaS. This workshop is designed for cloud and platform architects, DevOps, and SREs to learn more about how F5 NGINXaaS for Azure works - how it is configured, deployed, monitored, and managed.

Using various Azure Resources like virtual machines (VMs), containers, Azure Kubernetes Service (AKS) clusters, and Azure networking, you will deploy cloud applications and configure F5 NGINXaaS to deliver them using various real-world scenarios.

 

Load Balancing / Blue-Green / Split Clients / Multi-Cluster Load Balancing Lab

To learn more about and practice with implementing load balancing and advanced traffic management, we recommend the following lab: NGINX Load Balancing / Blue-Green / Split Clients / Multi Cluster LB.

In this lab, you will configure F5 NGINXaaS for proxying and load balancing across several different backend systems, including F5 NGINX Ingress Controllers in Azure Kubernetes Services (AKS) and a Windows virtual machine (VM). You will work with the F5 NGINXaaS configuration files to enable connectivity to your web applications running in the Docker containers, VMs, and AKS pods. You will also optionally configure load balancing for a Redis in-memory cache running in the AKS cluster.

During the lab, you will:

• Configure F5 NGINXaaS to proxy and load balance across AKS workloads
• Configure F5 NGINXaaS to proxy to a Windows server VM
• Test access to your F5 NGINXaaS configurations with Curl and Chrome
• Inspect the HTTP content
• Run an HTTP load test on your systems
• Enable HTTP Split Clients for blue/green deployments and A/B testing
• Configure F5 NGINXaaS for Redis Cluster (Optional)

Upon completion of this lab exercise, you will: 

• Hands-on experience in configuring advanced load balancing with F5 NGINXaaS for Azure.
• Enable F5 NGINXaaS to distribute traffic across different backend systems in advanced traffic-splitting scenarios, such as blue-green and canary deployments. 
• Gain practical knowledge in implementing multi-cluster load balancing for AKS using F5 NGINXaaS.

 

Conclusion

F5 NGINXaaS for Azure is a game-changer for organizations striving to enhance user experiences while simplifying operations. By offering adaptive load balancing, proactive health checks, advanced traffic management patterns like blue-green and canary deployments, and multi-cluster support, it aligns perfectly with the growing demands of cloud-native application development and deployment.