Uploaded December 2025 | Updated September 2026, 5 minutes ago
This video demonstrates how to migrate an application between heterogeneous environments using F5 Distributed Cloud.
The features used in this demo are Multicloud Networking Network Connect to use Customer Edge (CE) sites on VMware, Nutanix, RedHat OpenShift, and Azure. With the CEs online and the application running on the new platforms, Origin Pools are configured and added to a common Multicloud Networking App Connect load balancer. This provides connectivity to secure and deliver the app in each new location while providing the control and flexibility to move traffic away from one platform and onto others.
By allowing applications to be uniformly expanded and delivered with Distributed Cloud, organizations are more quickly able to move between platforms in order to meet new business requirements.
0:00 Introduction
0:47 Demo Overview
1:17 Configuration
1:39 Dashboard and traffic graphs
2:01 Additional resources
2:17 Conclusion
The following article provides more information about this setup, and includes a step-by-step workflow guide on how to migrate an application off of one platform and on to others.
community.f5.com/kb/technicalarticles/app-migration-across-heterogeneous-environments-using-f5-distributed-cloud/344192
Notes:
- Contributed by: Dave Potter
- Related Article: community.f5.com/kb/technicalarticles/app-migration-across-heterogeneous-environments-using-f5-distributed-cloud/344192
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👋 Chase Abbott: linkedin.com/in/chaseabbott1
This video demonstrates how to migrate an application between heterogeneous environments using F5 Distributed Cloud.
The features used in this demo are Multicloud Networking Network Connect to use Customer Edge (CE) sites on VMware, Nutanix, RedHat OpenShift, and Azure. With the CEs online and the application running on the new platforms, Origin Pools are configured and added to a common Multicloud Networking App Connect load balancer. This provides connectivity to secure and deliver the app in each new location while providing the control and flexibility to move traffic away from one platform and onto others.
By allowing applications to be uniformly expanded and delivered with Distributed Cloud, organizations are more quickly able to move between platforms in order to meet new business requirements.
0:00 Introduction
0:47 Demo Overview
1:17 Configuration
1:39 Dashboard and traffic graphs
2:01 Additional resources
2:17 Conclusion
The following article provides more information about this setup, and includes a step-by-step workflow guide on how to migrate an application off of one platform and on to others.
community.f5.com/kb/technicalarticles/app-migration-across-heterogeneous-environments-using-f5-distributed-cloud/344192
Notes:
- Contributed by: Dave Potter
- Related Article: community.f5.com/kb/technicalarticles/app-migration-across-heterogeneous-environments-using-f5-distributed-cloud/344192
⬇️⬇️ JOIN THE COMMUNITY! ⬇️⬇️⬇️
DevCentral is an online community of technical peers dedicated to learning, exchanging ideas, and solving problems—together.
Find all our platform links ⬇️ and follow our Community Evangelists! 👋
➡️ DEVCENTRAL: community.f5.com
➡️ YOUTUBE: youtube.com/devcentral
➡️ LINKEDIN: linkedin.com/showcase/f5-devcentral
➡️ X: https://x.com/devcentral
Your Community Evangelists:
👋 Jason Rahm: linkedin.com/in/jrahm | https://x.com/jasonrahm
👋 Buu Lam: linkedin.com/in/buulam | https://x.com/buulam
👋 Aubrey King: linkedin.com/in/aubreyking | https://x.com/aubreykingf5
👋 Chase Abbott: linkedin.com/in/chaseabbott1
![Agentic Observability: Monitoring AI Traffic with F5 NGINX & OpenTelemetry
AI agents are fundamentally changing infrastructure traffic — and traditional load balancers are flying blind. In this demo, we show how to gain deep, real-time visibility into AI/MCP traffic natively using F5 NGINX and OpenTelemetry — with zero backend code changes.
Using NGINX JavaScript (NJS) at the proxy layer, we parse JSON streams in real time to extract tool names, client identities, and target MCP servers. Combined with OpenTelemetry and the golden signals (latency, throughput, error rates), you get a comprehensive observability layer for agentic AI infrastructure — exportable to any OTel-compatible backend.
🔗 Get the code and try it yourself: [Ihttps://github.com/nginx/nginx-mcp-js/tree/main]
🔗 MCP Blog: [https://blog.nginx.org/blog/introducing-agentic-observability-in-nginx-real-time-mcp-traffic-monitoring]
Timestamps:
0:00 – Introduction & the AI traffic problem
0:22 – Why AI agents are unpredictable (fan-out behavior)
0:46 – Three critical blind spots: delivery, UX, and observability
1:36 – The NGINX solution: native Layer 7 MCP inspection
2:07 – How it works: NJS parsing + OpenTelemetry golden signals
2:51 – Demo walkthrough: installation & NGINX config
3:12 – Extracting MCP metadata and mapping to OTel spans
3:53 – Running the load generator to simulate AI traffic
4:00 – Grafana dashboard overview
4:19 – Troubleshooting: isolating slow tools
4:46 – Identifying rogue AI clients by identity
5:05 – Monitoring backend MCP server health
5:32 – Wrap-up and next steps
Notes:
- Contributed by: Akash Ananthanarayanan
- Related Article: None
⬇️⬇️ JOIN THE COMMUNITY! ⬇️⬇️⬇️
DevCentral is an online community of technical peers dedicated to learning, exchanging ideas, and solving problems—together.
Find all our platform links ⬇️ and follow our Community Evangelists! 👋
➡️ DEVCENTRAL: https://community.f5.com
➡️ YOUTUBE: https://youtube.com/devcentral
➡️ LINKEDIN: https://www.linkedin.com/showcase/f5-devcentral/
➡️ X: https://x.com/devcentral
Your Community Evangelists:
👋 Jason Rahm: https://www.linkedin.com/in/jrahm/ | https://x.com/jasonrahm
👋 Buu Lam: https://www.linkedin.com/in/buulam/ | https://x.com/buulam
👋 Aubrey King: https://www.linkedin.com/in/aubreyking | https://x.com/aubreykingf5
👋 Chase Abbott: https://www.linkedin.com/in/chaseabbott1 Agentic Observability: Monitoring AI Traffic with F5 NGINX & OpenTelemetry](https://i.ytimg.com/vi/PydaFJirjj0/mqdefault.jpg)









