Methods and apparatus to execute a workload in an edge environment are disclosed. An example apparatus includes a node scheduler to accept a task from a workload scheduler, the task including a description of a workload and tokens, a workload executor to execute the workload, the node scheduler to access a result of execution of the workload and provide the result to the workload scheduler, and a controller to access the tokens and distribute at least one of the tokens to at least one provider, the provider to provide a resource to the apparatus to execute the workload.

The generation, actuation, and enforcement of policies within a distributed computing system is provided. The policies are employed to manage the resources of the system. The resources include virtualized resources, such as virtual machines (VMs) and virtual storage disks (VSDs). A policy includes a rule and scope. Enforcing a policy includes applying the rule to resources that are within the policy's scope. Policies are employed to constrain the leasing period and reclaim leased resources, as well constrain the access of certain users to specific operations on the leased resources. Policies may be created via a UI that automatically generates a policy encoding. The policy is registered and accessed via a policy store. When multiple policies target a common resource, merging strategies are applied to the multiple policies. The multiple policies are ranked, merged, filtered, and any remaining conflicts are resolved to generate an effective policy that is consistent with the multiple policies and is enforced on the common resource.

Apparatuses, systems, and methods related to memory pooling between selected memory resources are described. A system using a memory pool formed as such may enable performance of functions, including automated functions critical for prevention of damage to a product, personnel safety, and/or reliable operation, based on increased access to data that may improve performance of a mission profile. For instance, one apparatus described herein includes a memory resource, a processing resource coupled to the memory resource, and a transceiver resource coupled to the processing resource. The memory resource, the processing resource, and the transceiver resource are configured to enable formation of a memory pool between the memory resource and another memory resource at another apparatus responsive to a request to access the other memory resource transmitted from the processing resource via the transceiver.

Systems and methods for transaction/file-based management of a plurality of processes associated with various jobs are provided. Through the management of discrete applications, a file distribution manager/scheduler orchestrates automated execution of different types of jobs. The processes executed for the various processes can vary based on job type, or other parameters.

Systems and methods are provided for improving compute job distribution using federated computing nodes. This includes identifying a plurality of independently controlled computing nodes which then receive a token such that they can each be identified as being authorized to participate in a federated computing node cluster. Metrics associated with the particular nodes are then received and based on the received metrics compute jobs are assigned to the particular node by assembling a compute job data packet comprising the one or more compute jobs and transmitting the assembled compute job data packet to the particular node. Other features are also described in which assigned compute jobs and/or unrelated compute tasks can be dynamically modified in order to optimize compute job completion based on the received metrics.

Apparatuses, systems, and methods related to memory pooling between selected memory resources are described. A system using a memory pool formed as such may enable performance of functions, including automated functions critical for prevention of damage to a product, personnel safety, and/or reliable operation, based on increased access to data that may improve performance of a mission profile. For instance, one apparatus described herein includes a memory resource, a processing resource coupled to the memory resource, and a transceiver resource coupled to the processing resource. The memory resource, the processing resource, and the transceiver resource are configured to enable formation of a memory pool between the memory resource and another memory resource at another apparatus responsive to a request to access the other memory resource transmitted from the processing resource via the transceiver.

Techniques for implementing rollback of infrastructure changes in an infrastructure orchestration service are described. In certain examples, an infrastructure orchestration service is disclosed that manages both provisioning and deploying of infrastructure assets within a cloud environment. The service receives a plan comprising a set of instructions associated with a set of infrastructure assets of an execution target and identifies a first state of the set of infrastructure assets. The service executes the set of instructions in the plan to achieve a second state for the set of infrastructure assets. Based in part on the executing, the service receives a trigger for rolling back the plan to restore the set of infrastructure assets in the plan to the first state and executes a rollback plan for the plan. The service then transmits a result associated with the execution of the rollback plan.

A method, a computing device, and a non-transitory machine-readable medium for managing modes of operation for volumes in a node. A first portion of a plurality of volumes in a node is selected to operate in an active mode. A second portion of the plurality of volumes in the node is selected to operate in a passive mode. The second portion of the volumes that operates in the passive mode consumes fewer resources than the first portion of the volumes that operates in the active mode. The first portion of the plurality of volumes and the second portion of the plurality of volumes are adjusted over time based on activity of each volume of the plurality of volumes.

Systems and methods for programming field programmable gate array (FPGA) devices are provided. A trained model for a deep learning process is obtained and converted to design abstraction (DA) code defining logic block circuits for programming an FPGA device. Each of these logic block circuits represents one of a plurality of modules that executes a processing step between different layers of the deep learning process.

Systems and methods scale an instance group of a computing platform by determining whether to scale up or down the instance group by using historical data from prior jobs wherein the historical data includes one or more of: a data set size used in a prior related job and a code version for a prior related job. The systems and methods also scale the instance group up or down based on the determination. In some examples, systems and methods scale an instance group of a computing platform by determining a job dependency tree for a plurality of related jobs, determining runtime data for each of the jobs in the dependency tree and scaling up or down the instance group based on the determined runtime data.