Intel Corporation

Joseph Butler

Thijs Metsch

Giovani Estrada

Alexander J Leckey

Michael J McGrath

Victor Bayon-Molino

John Kennedy

Katalin Klara Bartfai-Walcott

Slawomir Putyrski

Radhika Loomba

Vincenzo Mario Riccobene

Justin E Gottschlich

Mohammad Mejbah Ul Alam

Annie Ibrahim Rana

Connor Upton

Grigorios Katsaros

Chris Woods

Keith A Ellis

Leonard Feehan

Perumal Kappuudaiyar

Kevin Mullery

Michael Christopher Woods

Michael J Mcgrath

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.

Methods and apparatus to optimize workflows

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.

Automated resource management for distributed computing

Methods, systems, and storage media for telemetry adaptation are disclosed herein. In an embodiment, a networking device may include a data collector agent module to receive measurement data from measurement sources according to an initial telemetry policy and to provide the measurement data to the one or more servers of the monitoring system. The networking device may include an anomaly detection module to receive measurement data from the data collector agent module, to detect an anomaly in the measurement data, and to provide an indication of the anomaly to the data collector agent module for the data collector agent module to provide a first modified telemetry policy for the measurement sources.

Telemetry adaptation

There is disclosed in one example a computing apparatus, including: a hardware platform; and a service chain pre-placement analyzer to operate on the hardware platform and configured to: receive a total utility input for a service chain placement; predict a mobility pattern for the service chain placement; and compute an average utility for the service chain placement, wherein the average utility is a product of the total utility and the mobility pattern.

Mobile service chain placement

Apparatuses and methods associated with management of computing infrastructure resources and workloads are disclosed herein. In embodiments, an apparatus may comprise a resource-workload manager having: a placement solution generator to generate a plurality of potential resource placement solutions for allocation of various resources of the computing infrastructure to various workloads; one or more utility function calculators to calculate one or more values for one or more provider-centric attributes, and one or more values for one or more customer-centric attributes, for the plurality of potential solutions; an analyzer to analyze the attributes and select one of the potential resource placement solutions; and a resource allocator to allocate the resources, based at least in part on the selected resource placement solution. Other embodiments may be described and/or claimed.

Computing infrastructure resource-workload management methods and apparatuses

Techniques for dynamically adapting cloud applications are described. A current value of a Key Performance Indicator (KPI) of an executing cloud service instance of a cloud application is detected. A determination is made as to whether the current value of the KPI is unacceptable (e.g., either it is outside of an acceptable range or is within an adaptation region). If the current value of the KPI is unacceptable, a first application programming interface (API) is invoked to request a cloud service instance monitor to adapt the cloud service instance to operate within an acceptable KPI range. If the cloud service instance monitor was unsuccessful, the unacceptable KPI is escalated to the cloud service monitor for the type of the cloud service instance. If the cloud service monitor is unsuccessful, the unacceptable KPI is escalated to the application monitor.

Dynamically adapting cloud applications

Technologies for datacenter management include one or more computing racks each including a rack controller. The rack controller may receive system, performance, or health metrics for the components of the computing rack. The rack controller generates regression models to predict component lifespan and may predict logical machine lifespans based on the lifespan of the included hardware components. The rack controller may generate notifications or schedule maintenance sessions based on remaining component or logical machine lifespans. The rack controller may compose logical machines using components having similar remaining lifespans. In some embodiments the rack controller may validate a service level agreement prior to executing an application based on the probability of component failure. A management interface may generate an interactive visualization of the system state and optimize the datacenter schedule based on optimization rules derived from human input in response to the visualization. Other embodiments are described and claimed.

Workload optimization, scheduling, and placement for rack-scale architecture computing systems

One embodiment provides an apparatus. The apparatus includes ranker logic. The ranker logic is to rank each of a plurality of compute nodes in a data center based, at least in part, on a respective node score. Each node score is determined based, at least in part, on a utilization (U), a saturation parameter (S) and a capacity factor (C). The capacity factor is determined based, at least in part, on a sold capacity (C) related to the compute node. The ranker logic is further to select one compute node with a highest node score for placement of a received workload.

Ranking system

A system comprises a scoring engine comprising at least one processor and memory. The scoring engine is to generate, based on telemetry information obtained from a plurality of nodes of a computing infrastructure, a first availability score for a first node of the plurality of computing infrastructure nodes and a second availability score for a second node of the plurality of computing infrastructure nodes. The scoring engine is further to generate, based on the first availability score of the first computing infrastructure node and the second availability score of the second computing infrastructure node, an edge tension score for a link between the first node and the second node.

Computing infrastructure optimizations based on tension levels between computing infrastructure nodes

Examples may include techniques for allocating configurable computing resources from a pool of configurable computing resources to a logical server or virtual machine. The logical server or virtual machine may use allocated configurable computing resources to implement, execute or run a workload.

Techniques to allocate configurable computing resources

Examples are described for computing resource discovery and management for a system of configurable computing resources that may include disaggregate physical elements such as central processing units, storage devices, memory devices, network input/output devices or network switches. In some examples, these disaggregate physical elements may be located within one or more racks of a data center.

Growing community of inventors

Stamullen, Ireland

Joseph Butler