Merge pull request #180434 from lobrien/patch-mir-limits

Added section per Zhiyong

Author: PRMerger5

articles/machine-learning/how-to-troubleshoot-online-endpoints.md

@@ -236,6 +236,15 @@ Although we do our best to provide a stable and reliable service, sometimes thin
 
 If you are having trouble with autoscaling, see [Troubleshooting Azure autoscale](../azure-monitor/autoscale/autoscale-troubleshoot.md).
 
+## Bandwidth limit issues
+
+Managed online endpoints have bandwidth limits for each endpoints. You find the limit configuration in [Manage and increase quotas for resources with Azure Machine Learning](how-to-manage-quotas.md#azure-machine-learning-managed-online-endpoints-preview) here. If your bandwidth usage exceeds the limit, your request will be delayed. To monitor the bandwidth delay:
+
+- Use metric “Network bytes” to understand the current bandwidth usage. For more information, see [Monitor managed online endpoints](how-to-monitor-online-endpoints.md).
+- There are two response trailers will be returned if the bandwidth limit enforced: 
+    - `ms-azureml-bandwidth-request-delay-ms`: delay time in milliseconds it took for the request stream transfer.
+    - `ms-azureml-bandwidth-response-delay-ms`: delay time in milliseconds it took for the response stream transfer.
+
 ## HTTP status codes
 
 When you access online endpoints with REST requests, the returned status codes adhere to the standards for [HTTP status codes](https://aka.ms/http-status-codes). Below are details about how endpoint invocation and prediction errors map to HTTP status codes.