A computing environment includes a computing service provider and an edge computing network that has computing and storage devices that extend computing resources of the computing service provider to remote users. An edge platform is configured to execute industry specific PaaS services and industry specific third-party applications. The edge platform communicates with an IoT edge service, inference engine, and IoT edge gateway that performs protocol conversion. The industry edge platform provides a standardized platform for enabling execution of the industry specific PaaS services and industry specific third-party applications.

Methods, computer program products, and/or systems are provided that perform the following operations: identifying, in an environment that includes a plurality of edge clusters of edge nodes, a first edge cluster having a resource gap; broadcasting a resource requirement of the first edge cluster to other edge clusters in the plurality; obtaining resource commitments from one or more of the other edge clusters; selecting edge cluster resources from the one or more of the other edge clusters based, at least in part, on the resource commitments; and creating a new cluster including the first edge cluster and the selected edge cluster resources.

A system and method for distributing resources in a computing system is disclosed. The resources include hardware components in a hardware pool, a management infrastructure, and an application. A telemetry system is coupled to the resources to collect operational data from the operation of the resources. A data analytics system is coupled to the telemetry subsystem to predict a future operational data value based on the collected operational data. A policy engine is coupled to the data analytics system to determine a configuration to allocate the resources based on the future operational data value.

Multi-cloud deployment strategy is based on automated analysis of context and requirements for an activity workload. The activity workload is defined by user input including information regarding project cost, performance requirements, and geographical preferences. Selection of cloud-based resources for handling the activity workload is based in part on service availability record, projected cost of resources, and physical geographic locations. A cloud services registry provides cloud service provider data for selection to perform aspects of the activity workload.

Embodiments of systems and methods for managing compute resources and runtime object load status in a platform framework are described. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: receive, at a platform framework via an Application Programming Interface (API), an arbitration policy; notify an application, by the platform framework via the API, of a state change with respect to the arbitration policy based upon a change in context; receive, at the platform framework from the application via the API, an identification of at least one compute resource to execute a workload associated with the arbitration policy; and offload the workload to the compute resource.

A method may include allocating, based on a first load requirement of a first tenant, a first bin having a fixed capacity for handing the first load requirement of the first tenant. In response to the first load requirement of the first tenant exceeding a first threshold of the fixed capacity of the first bin, packing a second bin allocated to handle a second load requirement of a second tenant. The second bin may be packed by transferring, to the second bin, the first load requirement of the first tenant based on the transfer not exceeding the first threshold of the fixed capacity of the second bin. In response to the transfer exceeding the first threshold of the fixed capacity of the second bin, allocating a third bin to handle the first load requirement of the first tenant.

Adaptive control of deadline-constrained workload migrations can include monitoring migrations of workloads forming a wave migrating from a source computing node to a target computing node. The monitoring can be performed in real time. The migrations can be performed by transferring image replications of each workload over a data communication network. Based on an expected bandwidth availability, a likelihood that a cutover deadline associated with the wave is exceeded prior to completing a migration of each of the wave's workloads can be predicted. Migration of one or more selected workloads can be suspended in response to determining that exceeding the cutover deadline prior to completing migration of each of the wave's workloads is likely.

In one example in accordance with the present disclosure, an electronic device is described. An example electronic device includes a processor and memory storing executable instructions that when executed cause the processor to determine availability of memory resources and processing resources of multiple computing devices. The instructions also cause the processor to form a computing cluster based on the availability of the memory resources and the processing resources. The instructions further cause the processor to assign a computing task to the computing cluster to replace a cloud service.

A digital object may be routed via a network. Routing of a digital object may be based in part on a requested service, and/or on an ability of an intermediate node to provide the requested service, and/or on a willingness of the intermediate node to provide the requested service.

Embodiments of the present disclosure relate to a method and apparatus for balancing loads, and a computer-readable storage medium. The method includes: for each data processing unit in a set of data processing units in a data processing system, acquiring current input data of the data processing unit for a current clock cycle and next input data of the data processing unit for a next clock cycle; and determining a first metric value indicating changes in input data of said data processing unit in the next clock cycle based on a comparison between the current input data and the next input data. The method further includes controlling an operating state of the set of data processing units in the next clock cycle based on the first metric value determined for the set of data processing units.