The present disclosure pertains to a system configured to generate output data from a series of configurable cloud-computing processing operations. Some embodiments may; obtain, from a first database, data in a first format; process, in a first stage, the obtained data such that the obtained data is normalized; process, in a second stage, data in a second format different from the first format using a first set of cloud-computing devices; and detect, via a user interface, a first selection of at least two processing operations, the first selection being made from among a plurality of available processing operations. Each of the processing stages may include input data, a processing operation, and output data.

A method for maintaining version consistency of resources. The method provides for one or more processors to receive a submitted request to run a job in which the job includes a processing element and a timestamp associated with running the job. Identification of a resource type associated with the processing element is determined, based on a tag included in the job, associated with the processing element. A version of the resource type of the processing element is determined, based on a mapping of the tag associated with the identified resource type and the timestamp of the job. The resource type of the determined version is requested from a resource manager, and responsive to a confirmation of assigning the version of the resource type from the resource manager, the process element of the job is performed on the version of the resource type assigned by the resource manager.

A system for ANN training through processing power of parked vehicles. The system can include a master computing device having a controller configured to control training of an ANN. The training can be performed at least partially in separate parts by computing devices of parked vehicles. The controller can be configured to separate computing tasks of training the ANN into separated tasks. Also, the controller can be configured to assign at least some of the separated tasks to selected computing devices of parked vehicles. The controller can also be configured to receive and assemble results of the separated tasks to train the ANN. The controller can also be configured to train the ANN according to the results. The master computing device can be configured to send the assigned tasks to the selected devices of the vehicles as well as receive, from the selected devices, the results of the assigned tasks.

This disclosure is directed to systems and methods for determining target allocation parameters for initiating targeted communications in complex computing networks, which may be associated with the allocation of allocatables in execution events over a period of time. The systems and methods may include receiving a desired allocation; determining a first available allocation at a first time; generating allocation information for a second period comprising the first time; determining a second available allocation at a second time; determining a remaining available allocation, based on the allocation information and the second available allocation; and determining one or more target allocation parameters for initiating a targeted communication to a computing device after the second time.

A method of initiating a transfer of an active first-type slave process, executed in a first processing entity of an edge cloud computing layer, to a second processing entity of the edge cloud computing layer, includes, at a first mobile entity, receiving a first heat map relating to the first-type master-slave process, ranking, based on a cost function, possible process sharing connections, between the first mobile entity and one or more second processing entities, for the current location of the first mobile entity and/or a location of the first mobile entity in the near future, determining, based on the ranking, one or more second processing entities as potential target processing entities to transfer the first-type slave process to, and transmitting a processing entity transfer request to a control process executed in the edge cloud computing layer. The request includes an identification of the active first-type slave process and indicates at least one of the second processing entities determined, based on the ranking, as potential target to transfer the active first-type slave process to.

Interface circuitry to detect a request to obtain a resource request from a workload and processor circuitry including one or more of: at least one of a central processing unit, a graphic processing unit or a digital signal processor, the at least one of the central processing unit, the graphic processing unit or the digital signal processor having control circuitry, arithmetic and logic circuitry, and one or more registers, the processor circuitry to execute instructions to: determine if resources are available for the workload on an infrastructure processing unit managed system; negotiate with the infrastructure processing unit to determine if an executing workload can be migrated; in response to determining that an executing workload can be migrated, cause the executing workload to be migrated; and cause the workload to execute on the resource.

In some embodiments, a method for containerized application deployment to use multi-cluster computing resources may include receiving a request to deploy a containerized application, The request may be associated with a resource specification that indicates that, when deployed, the containerized application uses a first computing resource and a second computing resource. Accordingly, after determining an availability of the first computing resource on a first duster and of the second computing resource on a different second duster, the method may further include deploying the containerized application based on the determined availabilities of the computing resources. For example, the method may include deploying the containerized application such that it uses the first computing resource on the first duster and the second computing resource on the second duster. Corresponding methods and systems are also disclosed.

A priority-based resource allocation method, includes accepting a resource application submitted by a job, the resource application including resource demand information and job priority information; determining, according to the resource demand information of the resource application, whether remaining resources of a system meet the resource application, and traversing, in an allocated resource application queue when the remaining resources do not meet the resource application, allocated resource applications having job priorities lower than that of the resource application; using the sum of system resources occupied by all traversed resource applications plus the remaining resources as available resources; and stopping traversing when the available resources meet the resource application, and allocating the available resources to the resource application. The technical solution of the present disclosure enables a resource application having a high job priority to preempt resources of a resource application having a low job priority.

A cloud marketplace system can be configured to communicate with multiple cloud computing environments in order to ascertain the details for the resources and services provided by the cloud computing environments for optimizing resources utilized by virtual machines. The cloud marketplace system can be configured to determine the resource and service data for the cloud computing environments and select a set of resource servers for instantiating the virtual machines based specifications of the virtual machines and parameters of the instantiation. The cloud marketplace system can be configured to periodically monitor the cloud's resources and migrate the virtual machines if resources become available that more closely match the parameters of the virtual machines.

A technique for managing pooled resources in a distributed computing environment includes a resource manager that receives resource allocation requests from workload managers for running one or more applications. The applications are initiated by a consumer through at least one of the workload managers where each workload manager is configured to translate demand requests from the applications into resource allocation requests. A determination is made whether there are available resources to satisfy the resource allocation requests. Responsive to determining that there is an unmet resource allocation request, a candidate resource meeting some but not all attributes needed to satisfy the unmet resource allocation request is identified and reconfigured to have the attributes needed to satisfy the unmet resource allocation request.