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 to investigate the problem and you decide to set the autoscale_target_utilization parameter of your AksWebservice object in your code to 80
Does it solve the problem?
Click on the arrows to vote for the correct answer
A. B.Answer: B.
Option A 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 to 80, this margin narrows, further decreasing the resistance against peak demands, hence the answer is incorrect.
So it does NOT solve the problem.
Option B is CORRECT because the default setting for autoscale target utilization is 70%
By decreasing it, the flexibility increases, i.e.
the infrastructure can accommodate higher fluctuations without running out of capacity.
Therefore, this is the correct answer.
Reference:
Setting the autoscale_target_utilization parameter to 80 on the AksWebservice object may or may not solve the problem of clients experiencing HTTP 503 (Service Unavailable) errors.
The autoscale_target_utilization parameter is used to define the target utilization percentage of the service instance. The Azure Kubernetes Service will use this target utilization percentage to determine the number of service instances required to handle the incoming traffic. If the utilization percentage is too high, the service may become overloaded and cause HTTP 503 errors.
Setting the autoscale_target_utilization parameter to 80 means that the Azure Kubernetes Service will try to maintain 80% utilization of the service instance. If the utilization goes above this percentage, the Azure Kubernetes Service will automatically scale up the service instance to handle the traffic.
However, the root cause of the HTTP 503 errors may be due to a variety of factors, such as insufficient memory or CPU resources, network issues, or bugs in the code. Therefore, it is not guaranteed that setting the autoscale_target_utilization parameter to 80 will solve the problem.
As a data engineer, it is important to investigate further to identify the root cause of the HTTP 503 errors and take appropriate actions to resolve them. This may involve monitoring the service instance for resource utilization, analyzing logs for errors, and debugging the code.