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: 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 reactive intent-driven end-to-end (E2E) orchestration are described herein. An orchestrator system, includes a processor; and memory to store instructions, which when executed by the processor, cause the system to: receive, at the orchestrator system, an intent-based service level agreement (SLA) for execution of a series of tasks on a plurality of compute nodes; calculate, based on the intent-based SLA, intermediate latency thresholds corresponding to each task of the series of tasks; calculate slack estimates based on the latency thresholds and real-time telemetry of the plurality of compute nodes or real-time telemetry of connections between the plurality of compute nodes; monitor execution of the series of tasks on the plurality of compute nodes; and perform a corrective action in response to determining that the execution of the series of tasks is predicted to exceed one of the intermediate latency thresholds.

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 computational storage are described herein. An orchestrator system is configured to: receive, at the orchestrator system, a registration package, the registration package including function code, a logical location of input data for the function code, and an event trigger for the function code, the event trigger set to trigger in response to when the input data is modified; interface with a storage service, the storage service to monitor the logical location of the input data and notify a location service when the input data is modified; interface with the location service to obtain a physical location of the input data, the location service to resolve the physical location from the logical location of the input data; and configure the function code to execute near the input data.

Abstract: An example apparatus includes interface circuitry to obtain a power draw of a node, telemetry data of a core in the node, and an application characteristic of an application, and a programmable circuit to determine a first weight based on the telemetry data and the application characteristic, the first weight indicative of an impact the application has on a power consumption of the core, determine a second weight based on the application characteristic, the second weight indicative of an impact the application has on a power consumption of resources accessible by the core and by different cores of the node, determine a power estimate value for the application based on the first weight, the second weight, the power consumption of the core, and the power consumption of the resources, the power estimate value indicative of how much power of the power draw of the node that the application draws.

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-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: 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 systems and methods are described for implementing security intents for the execution of workloads in cloud-to-edge (C2E) and cloud-native execution environments. An example technique for implementing security intents for a workload on a computing node of a cluster includes: identifying a workload for execution on the computing node; identifying security intents that define levels of respective security requirements for the execution of the workload on the computing node; adapting an execution environment of the computing node, based on the identified security intents; and controlling the execution of the workload within the execution environment, based on the identified security intents, to dynamically monitor and adapt to changing security conditions during the execution of the workload.

Abstract: Various systems and methods for autonomously monitoring intent-driven end-to-end (E2E) orchestration are described herein. An orchestration system is configured to: receive, at the orchestration system, an intent-based service level objective (SLO) for execution of a plurality of tasks; generate a common context that relates the SLO to the execution of the plurality of tasks; select a plurality of monitors to monitor the execution of the plurality of tasks, the plurality of monitors to log a plurality of key performance indicators; generate a domain context for the plurality of tasks; configure an analytics system with the plurality of monitors and the plurality of key performance indicators correlated by the domain contexts; deploy the plurality of monitors to collect telemetry; monitor the execution of the plurality of tasks using the telemetry from the plurality of monitors; and perform a responsive action based on the telemetry.

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: Systems, apparatus, articles of manufacture, and methods are disclosed. An example apparatus includes programmable circuitry to at least: obtain a first response associated with an estimate of emissions to be produced by execution of a workload on first hardware; obtain a second response associated with an estimate of emissions to be produced by execution of the workload on second hardware; and assign one of the first or the second hardware to execute the workload based on the first response and the second response, the assigned one of the first or the second hardware to at least one of utilize more time or more memory to execute the workload than the other of the first or the second hardware.

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.