Abstract: An example disclosed apparatus comprises a trigger monitor to detect an event satisfying a cache scrape trigger rule during execution of a workload, and a cache scraper to scrape cache data from cache in hardware during the execution of the workload.

Abstract: In one embodiment, an apparatus includes a plurality of processing cores, where each processing core is capable of executing at least one of a subset of an instruction set architecture (ISA). The apparatus also includes hardware circuitry to determine, during runtime, whether a thread comprising instructions of a particular ISA subset can execute on a particular processing core, and based on the determination, indicate a capability of the thread to be executed on the particular processing core in subsequent executions.

Abstract: A device may include a memory and a processor configured to obtain cell data representative of one or more attributes associated with a state of a cell of a mobile communication network, wherein radio resources of the cell are managed based on a radio resource management model, determine, for the radio resource management model, an update timescale based on the cell data, and communicate timescale information representative of the determined update timescale to the radio resource management model, wherein the timescale information is configured to cause the radio resource management model to update model parameters to manage the radio resources of the cell based on the determined update timescale.

Abstract: Methods, apparatus, and systems are disclosed for mapping active assurance intents to resource orchestration and life cycle management. An example apparatus disclosed herein is to reserve a probe on a compute device in a cluster of compute devices based on a request to satisfy a resource availability criterion associated with a resource of the cluster, apply a risk mitigation operation based on the resource availability criterion before deployment of a workload to the cluster, and monitor whether the criterion is satisfied based on data from the probe after deployment of the workload to the cluster.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed that optimize workflows. An example apparatus includes an intent determiner to determine an objective of a user input, the objective indicating a task to be executed in an infrastructure, a configuration composer to compose a plurality of workflows based on the determined objective, a model executor to execute a machine learning model to create a confidence score relating to the plurality of workflows, and a workflow selector to select at least one of the plurality of workflows for execution in the infrastructure, the selection of the at least one of the plurality of workflows based on the confidence score.

Abstract: Systems, apparatus, and methods for managing cooling of compute components are disclosed. An example apparatus includes programmable circuitry to at least one of instantiate or execute machine readable instructions to identify a workload to be performed by a compute device, identify a service level objective associated with the workload or the compute device, determine a parameter of a coolant to enable the service level objective to be satisfied during performance of the workload, and cause a cooling distribution unit to control the coolant based on the coolant parameter.

Abstract: In some embodiments, infrastructure data and service data is received for a computing infrastructure. The infrastructure data indicates resources in the computing infrastructure, and the service data indicates services to be orchestrated across the computing infrastructure. An infrastructure capacity model is generated, which indicates a capacity of the computing infrastructure over a particular time window. Service-to-resource placement options are also identified, which indicate possible placements of the services across the resources over the particular time window. Resource inventory data is obtained, which indicates an inventory of resources that are available to add to the computing infrastructure during the particular time window. An infrastructure capacity plan is then generated, which indicates resource capacity allocation options over the time slots of the particular time window. Resource capacities for the services are then allocated in the computing infrastructure.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed that optimize workflows. An example apparatus includes an intent determiner to determine an objective of a user input, the objective indicating a task to be executed in an infrastructure, a configuration composer to compose a plurality of workflows based on the determined objective, a model executor to execute a machine learning model to create a confidence score relating to the plurality of workflows, and a workflow selector to select at least one of the plurality of workflows for execution in the infrastructure, the selection of the at least one of the plurality of workflows based on the confidence score.

Abstract: Various approaches for the integration and use of edge computing operations in satellite communication environments are discussed herein. For example, connectivity and computing approaches are discussed with reference to: identifying satellite coverage and compute operations available in low earth orbit (LEO) satellites, establishing connection streams via LEO satellite networks, identifying and implementing geofences for LEO satellites, coordinating and planning data transfers across ephemeral satellite connected devices, service orchestration via LEO satellites based on data cost, handover of compute and data operations in LEO satellite networks, and managing packet processing, among other aspects.

Abstract: An apparatus of a computing node of a computing network, a method to be performed at the apparatus, one or more computer-readable storage media storing instructions to be implemented at the apparatus, and a system including the apparatus. The apparatus includes a processing circuitry to: receive, from an orchestration block, a first workload (WL) package including a WL and first computing resource (CR) metadata; recompose the first WL package into a second WL package that includes the WL and second CR metadata that is different from the first CR metadata, is based at least in part on CR information regarding a server architecture onto which the WL is to be deployed, and is further to indicate one or more processors of the server architecture onto which the WL is to be deployed; and send the second WL package to one or more processors of the server architecture for deployment of the WL thereon.

Abstract: A method for distributing at least one computational process amongst shared resources is proposed. At least two shared resources capable of performing the computational process are determined. According to the method, a workload characteristic for each of the shared resources is predicted. The workload characteristic accounts for at least two subsystems of each shared resource. One of the at least two shared resources is selected based on the predicted workload characteristics.

Abstract: Disclosed herein are guidance/supervision systems that may utilize guidance and supervision policies from various stakeholders, such as resource owner(s) and service owner(s), to provide guidance, supervision, and introspective analysis for an autonomous system that configures a group of shared resources. The system includes determining, based on predefined preferences associated with shared resources, a set of policies to guide an autonomous system in determining a set of configuration parameters for sharing the shared resources. The system also includes providing the set of policies to the autonomous system to configure the shared resources according to the set of configuration parameters.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to control processing of telemetry data at an edge platform. An example apparatus includes an orchestrator interface to, responsive to an amount of resources allocated to an orchestrator to orchestrate a workload at the edge platform meeting a first threshold, transmit telemetry data associated with the orchestrator to a computer to obtain a first orchestration result at a first granularity; a resource management controller to determine a second orchestration result at a second granularity to orchestrate the workload at the edge platform, the second granularity finer than the first granularity; and a scheduler to schedule a workload assigned to the edge platform based on the second orchestration result.

Abstract: In some embodiments, infrastructure data and service data is received for a computing infrastructure. The infrastructure data indicates resources in the computing infrastructure, and the service data indicates services to be orchestrated across the computing infrastructure. An infrastructure capacity model is generated, which indicates a capacity of the computing infrastructure over a particular time window. Service-to-resource placement options are also identified, which indicate possible placements of the services across the resources over the particular time window. Resource inventory data is obtained, which indicates an inventory of resources that are available to add to the computing infrastructure during the particular time window. An infrastructure capacity plan is then generated, which indicates resource capacity allocation options over the time slots of the particular time window. Resource capacities for the services are then allocated in the computing infrastructure.

Abstract: Methods, apparatus, systems and articles of manufacture to recommend instruction adaptations to improve compute performance are disclosed. An example apparatus includes a pattern detector to detect an execution pattern from an execution profile provided by a server, the execution profile associated with an instruction stored in an instruction repository. An adaptation identifier is to identify a possible instruction adaptation that may be applied to the instruction associated with the execution pattern. A model processor is to predict, using a machine learning model, an expected performance improvement of the adaptation. A result comparator is to determine whether the expected performance improvement meets an threshold. An instruction editor is to, in response to the result comparator determining that the expected performance improvement meets the threshold, apply the possible instruction adaptation to the instruction in the instruction repository.

Abstract: Various systems and methods for reactive intent-driven end-to-end (E2E) orchestration are described herein.

Abstract: Various systems and methods for implementing intent-based orchestration in heterogenous compute platforms are described herein. An orchestration system is configured to: receive, at the orchestration system, a workload request for a workload, the workload request including an intent-based service level objective (SLO); generate rules for resource allocation based on the workload request; generate a deployment plan using the rules for resource allocation and the intent-based SLO; deploy the workload using the deployment plan; monitor performance of the workload using real-time telemetry; and modify the rules for resource allocation and the deployment plan based on the real-time telemetry.

Abstract: Various systems and methods for implementing intent-based cluster administration are described herein. An orchestrator system includes: a processor; and memory to store instructions, which when executed by the processor, cause the orchestrator system to: receive, at the orchestrator system, an administrative intent-based service level objective (SLO) for an infrastructure configuration of an infrastructure; map the administrative intent-based SLO to a set of imperative policies; deploy the set of imperative policies to the infrastructure; monitor performance of the infrastructure; detect non-compliance with the set of imperative policies; and modify the administrative intent-based SLO to generate a revised set of imperative policies that cause the performance of the infrastructure to be compliant with the revised set of imperative policies.

Abstract: Various systems and methods for implementing intent-driven power management are described herein. A system includes: a power monitoring unit to collect real-time telemetry of a processor on a compute node; and a power level controller to: receive a power intent for execution of an application on the compute node; configure a power level of the processor of the compute node based on the power intent, the processor to execute the application; set an initial execution priority of the application on the compute node based on the power intent; and modify the initial execution priority based on the power intent and the real-time telemetry of the compute node.

Abstract: Various systems and methods for implementing reputation management and intent-based security mechanisms are described herein.