A module optimizes active and inactive components and resources of a
hybrid computing
system to optimize a combination of private and public resources to minimize cost and maximize performance of the
hybrid computing
system. A learning model may analyze past usage and present usage
metrics of one or more components with respect to performance criteria or cost criteria. Cost factors associated with components of the private
system may be based on wear—the higher the wear the less desirable a component's use becomes due to lower reliability and higher warranty costs. When activating a component of the private system, a deactivation of a higher wear component may be delayed allowing time for a recently activated component to be integrated with the private system. A resource of a public system may be used while deactivation of a high wear component is delayed.