> We found that with an application that increases its memory utilization over time, low requests (which reserves memory for each pod) and a generous hard limit

> We found that with an application that increases its memory utilization over time, low requests (which reserves memory for each pod) and a generous hard limit on utilization was a recipe for node saturation and a high rate of evictions. To adjust for this we eventually decided to use higher requests and lower limit which took pressure off of the nodes and allowed pods to be recycled without putting too much pressure on the node. Kubernetes works at its most efficient when you scale by adding more Pods, rather than scaling within a Pod. Requests and limits should be equal to each other, and a HorizontalPodAutoscaler (or KEDA Scales object) should be used to add more Pods as utilization increases. Using Cluster Autoscaler then makes sure that your cluster has sufficient Nodes to schedule the Pods. Then you just need to set up alerting to tell you if the autoscaling reaches minimums (indicating that you should consider lowering the minimum, to improve efficiency) or maximums (indicating that you should consider increasing the maximums), and you can leave things well enough alone, unless the speed at which scaling occurs is insufficiently slow for your use case.