You have an Azure ML real-time inference model deployed to Azure Kubernetes Service.
While running the model, clients sometimes experience a HTTP 503 (Service Unavailable) error.
As a data engineer, you have started investigating the problem and you decide to set the autoscale_target_utilization parameter of your AksWebservice object in your code to 60
Does it solve the problem?
Click on the arrows to vote for the correct answer
A. B.Answer: A
Option A is CORRECT because the default setting for autoscale target utilization is 70%
By decreasing it to 60, the flexibility increases, i.e.
the infrastructure can accommodate higher fluctuations without running out of capacity.
Therefore, this is the correct answer.
Option B is incorrect becausethe utilization level used to trigger creating new replicas is set to 70%, by default, meaning that the “buffer” to handle fluctuations is the remaining 30%
By increasing the limit, the margin narrows, further decreasing the resistance against peak demands, hence the answer is incorrect.
So it does NOT solve the problem.
Reference:
Setting the autoscale_target_utilization parameter of the AKS (Azure Kubernetes Service) Webservice object to 60 may or may not solve the HTTP 503 error problem.
The autoscale_target_utilization parameter controls the scale-up behavior of the AKS cluster based on the current utilization level of the resources allocated to it. The parameter sets the target utilization level at which the AKS cluster should scale up or scale down. If the parameter is set to 60, then the AKS cluster scales up when the current utilization level reaches 60% of the allocated resources, and scales down when the utilization level drops below that level.
HTTP 503 (Service Unavailable) error occurs when the web server is unable to handle the request due to some internal server error or overload. It could be due to various reasons such as network latency, insufficient resources, or too many requests.
Setting the autoscale_target_utilization parameter to 60 may help in situations where the HTTP 503 error is caused by insufficient resources. By setting the target utilization level to 60, the AKS cluster scales up when the current utilization level reaches 60% of the allocated resources, which could help alleviate the resource constraint and reduce the HTTP 503 errors. However, if the HTTP 503 error is caused by network latency or too many requests, then changing the autoscale_target_utilization parameter may not have any effect on the error.
In summary, setting the autoscale_target_utilization parameter to 60 may or may not solve the HTTP 503 error problem, depending on the root cause of the error. It is important to investigate the root cause of the error and perform additional diagnostic and tuning measures to resolve the issue.