Technologies for allocating resources across data centers

At least one non-transitory machine-readable storage medium storing instructions for being executed by at least one machine, the at least one machine to be associated with a computing system, the computing system being configurable to be used with local data center resources in association with providing of at least one cloud-based service, the at least one cloud-based service being associated with execution of at least one workload, the local data center resources comprising local physical rack server resources that are to communicate with the computing system via communication links, the computing system comprising other data center resources remote from the local data center resources, the computing system also comprising management resources that comprise circuitry, the instructions, when executed by the at least one machine, resulting in the computing system being configured for performance of operations comprising:

• configuring the other data center resources to comprise compute resources, storage resources, and accelerator resources;
• and
• configuring the management resources for use in allocating and/or deallocating the compute resources, the storage resources, the accelerator resources, and/or the local physical rack server resources for use in the execution of the at least one workload;
• wherein:
• the at least one workload comprises at least one virtual machine workload and/or at least one container workload;
• the compute resources, the storage resources, and the accelerator resources are associated with at least one cloud service provider;
• the local physical rack server resources are at one or more premises associated with a customer of the at least one cloud service provider;
• the management resources are configurable to dynamically allocate and/or dynamically deallocate, on an as-needed basis, based upon (1) telemetry-based present resource utilization data, (2) future resource utilization prediction data, (3) application programming interface (API) data, (4) resource utilization trend data, and (5) quality of service-related data, the compute resources, the storage resources, the accelerator resources, and/or the local physical rack server resources for use in the execution of the at least one workload;
• the accelerator resources and the local physical rack server resources are configurable to comprise graphics processing unit circuits for use in the execution, at least in part, of the at least one workload;
• the at least one workload is configurable to perform at least one machine learning-related operation;
• the management resources are to select, based upon the API data, the compute resources, the storage resources, the accelerator resources, and/or the local physical rack server resources for dynamic allocation and/or dynamic deallocation for use in the execution of the at least one workload;
• the API data is configurable to be communicated to the management resources in accordance with multiple different protocols.

A method implemented using a computing system, the computing system being configurable to be used with local data center resources in association with providing of at least one cloud-based service, the at least one cloud-based service being associated with execution of at least one workload, the local data center resources comprising local physical rack server resources that are to communicate with the computing system via communication links, the computing system comprising other data center resources remote from the local data center resources, the computing system also comprising management resources that comprise circuitry, the method comprising:

• configuring the other data center resources to comprise compute resources, storage resources, and accelerator resources;
• and
• configuring the management resources for use in allocating and/or deallocating the compute resources, the storage resources, the accelerator resources, and/or the local physical rack server resources for use in the execution of the at least one workload;
• wherein:
• the at least one workload comprises at least one virtual machine workload and/or at least one container workload;
• the compute resources, the storage resources, and the accelerator resources are associated with at least one cloud service provider;
• the local physical rack server resources are at one or more premises associated with a customer of the at least one cloud service provider;
• the management resources are configurable to dynamically allocate and/or dynamically deallocate, on an as-needed basis, based upon (1) telemetry-based present resource utilization data, (2) future resource utilization prediction data, (3) application programming interface (API) data, (4) resource utilization trend data, and (5) quality of service-related data, the compute resources, the storage resources, the accelerator resources, and/or the local physical rack server resources for use in the execution of the at least one workload;
• the accelerator resources and the local physical rack server resources are configurable to comprise graphics processing unit circuits for use in the execution, at least in part, of the at least one workload;
• the at least one workload is configurable to perform at least one machine learning-related operation;
• the management resources are to select, based upon the API data, the compute resources, the storage resources, the accelerator resources, and/or the local physical rack server resources for dynamic allocation and/or dynamic deallocation for use in the execution of the at least one workload;
• and
• the API data is configurable to be communicated to the management resources in accordance with multiple different protocols.