Abstract: A cloud configuration, including public and private clouds, is organized hierarchically, with a top level and any number of lower levels. A parent level cloud receives resource workload metrics from respective child level cloud(s), makes predictions, based in part on the metrics, as to future resource needs at the child level. The parent level sets up runtime-modifiable business rules and scaling plans based on the predictions. The parent level cloud sends the scaling plans to respective child level(s). The parent level automatically triggers a scaling plan at a child level, if conditions written into the business rules are satisfied. Resources are scaled upward or downward automatically as needed to maintain optimal resource usage.

Abstract: A method includes generating, by a computing device, a probe; deploying, by the computing device, the probe into a virtual machine hosting a cloud application; increasing, by the computing device, resource consumption by the probe; collecting, by the computing device, performance data from the cloud application; and training, by the computing device, a machine learning model using the performance data.

Abstract: A cloud configuration, including public and private clouds, is organized hierarchically, with a top level and any number of lower levels. A parent level cloud receives resource workload metrics from respective child level cloud(s), makes predictions, based in part on the metrics, as to future resource needs at the child level. The parent level sets up runtime-modifiable business rules and scaling plans based on the predictions. The parent level cloud sends the scaling plans to respective child level(s). The parent level automatically triggers a scaling plan at a child level, if conditions written into the business rules are satisfied. Resources are scaled upward or downward automatically as needed to maintain optimal resource usage.