Abstract: 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.

Abstract: A fair and efficient guest to hypervisor virtual machine socket protocol may be provided by: in response to a host determining to reject a message received from a guest that was previously accepted for processing by the host, transmitting a rejection to the guest; in response to receiving, at the guest, the rejection, adding the message to a processing request queue on the guest; in response to determining that resources to handle the message have become available for the guest, transmitting an indication to the guest that the host is able to reaccept the message; in response to receiving, at the guest, the indication, retransmitting the message to the host according to the processing request queue; and in response to receiving the message from the guest a second time, accepting the message in an execution queue in a virtual memory of the host.