What routing and failover policies protect workloads on Azure Front Door from regional cable cuts?

The safest way to roll out updates on Azure Front Door without risking peak-hour outages is to use a canary deployment strategy by leveraging the weighted routing capabilities of your origin groups. This approach allows you to direct a small percentage of live traffic to the updated backend while monitoring its performance. If any issues are detected, traffic can be instantly reverted to the stable version with minimal user impact.

To proactively detect latency spikes on Azure Front Door (AFD) before customers notice, SREs should monitor a comprehensive dashboard in Azure Monitor that focuses on both edge and backend performance. The dashboard should combine AFD metrics with Application Insights and network health data to differentiate between problems with the AFD platform, backend services, or the broader internet.

• AFD metrics dashboard

This dashboard uses platform metrics collected by Azure Monitor to give a real-time view of AFD's performance. 

• Application performance dashboard

This dashboard uses Application Insights to provide an end-to-end view of your application, validating if the issue is with your backend services rather than AFD.

• Network and multi-region dashboard

Since AFD performance is tied to its global network, monitoring broader network health is crucial.

• Automation for proactive alerts

To catch latency spikes before customers do, configure Azure Monitor alerts on these dashboard metrics.

In light of recent, complex outages like the Cloudflare-AWS congestion and Azure Front Door (AFD) platform configuration errors, disaster recovery (DR) testing for AFD must move beyond simple backend failures. Your tests should now simulate broader network degradations and AFD platform-level issues to validate your full resilience strategy. 

• 1. Test backend failover under duress
• 2. Simulate AFD platform-level failure
• 3. Verify end-to-end resilience during a game day
• 4. Practice communication and documentation

The safest way to roll out updates on Amazon Bedrock without risking peak-hour outages is to use a canary deployment strategy. This technique involves directing a small, incremental percentage of live traffic to the new model or configuration while closely monitoring its performance. If any issues are detected, traffic can be instantly routed back to the stable version, mitigating risk during peak hours.
The blue/green method, while effective, can be less granular and riskier for AI models, where behavior changes are more subtle. Canary deployments provide fine-grained control and a real-world testing period.

To spot Amazon Bedrock latency spikes before they impact customers, SREs should focus on a multi-layered dashboard in Amazon CloudWatch. This approach uses Bedrock's native metrics, application performance data, and network-level insights to provide early warnings and help pinpoint the root cause of performance degradation.