Can someone tell me use cases that uses multiple combination of custom metrics to scale or combination of custom and system metrics?
when it comes to scaling in Kubernetes, we rely on metrics. By default, we use CPU and Memory metrics. Considering use cases where we need custom metrics (such as 'request count per second'), Kubernetes allows us to plug in custom metrics. For an application, there might be interrelated metrics that help in scaling. For example, combining "CPU" and "memory" metrics, we can use "CPU^2/memory" as the combined metric to scale. Can someone direct me towards real-world scenarios similar to this, but relating *custom metrics, where multiple *custom metrics are correlated in order to perform scaling? will this be a better approach?
License limits on number of simultaneous users will be an application metric that affect scaling for a group of users in a particular infrastructure. Reason for this is purely commercial. I have seen admins in that kind of environment try to optimize their environment to get their users to hold licenses as short as time as possible - much easier with batch oriented workloads than with interactive ones.
What does your question mean? What are application-level metrics? What does it mean to scale on application-level metrics? What does it mean to not scale on CPU and memory?
I think that are excluding the metrics about CPU and memory consuming when an application is running. I suppose that you refer to other type of metrics that the applications can generate internally. If are these latter, I can give as example one project where the web application produce metrics in order to improve the load balancing mechanism.
Another example is security. The log records are used to produce metrics to support security.
Rui Humberto Pereira Thank you for the answer.
https://www.researchgate.net/post/Creating_an_Autoscaling_Logic_using_multiple_metrics_in_Kubernetes
can you also look into this question?
@C.P. Ravikumar Further explanation is found here on this link mentioned above
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