I'm curious if there are any AI models designed for autoscaling in environments like Kubernetes. Typically, systems rely on straightforward thresholds, like scaling when CPU utilization hits 70%. However, I'm wondering if there are more advanced methods that use AI to determine scaling needs, potentially by analyzing patterns in usage data. I came across a concept called HPA+ but couldn't find much information. Any insights into relevant datasets, research papers, or approaches would be greatly appreciated!
1 Answer
What do you think AI can achieve that a set threshold can't? What's your motivation for moving away from thresholds?
I believe AI can analyze usage patterns to scale resources proactively, rather than just reacting once a threshold is crossed. For instance, if CPU usage is nearing a threshold, an AI model could intervene before reaching that point, which might save on unnecessary resource allocation. I'm researching this to see if such an approach is practical.