Intel Corporation

Thijs Metsch

Joseph Butler

Francesc Guim Bernat

Kshitij Arun Doshi

Ned M Smith

Rita H Wouhaybi

Giovani Estrada

Alexander J Leckey

Justin E Gottschlich

Adrian C Hoban

Annie Ibrahim Rana

John Kennedy

Radhika Loomba

Vincenzo Mario Riccobene

Mark D Yarvis

Mats Gustav Agerstam

Marcos Emanuel Carranza

Ravishankar Iyer

Srikathyayani Srikanteswara

John Joseph Browne

Katalin Klara Bartfai-Walcott

Slawomir Putyrski

Rahul Khanna

Timothy Verrall

Susanne M Balle

Stephen T Palermo

Yi Zhang

Neelam Chandwani

Mohammad Mejbah Ul Alam

Bin Li

Dario Oliver

Patrick Koeberl

Mikko Ylinen

Leonard Feehan

David Cremins

Michael Mesnier

Keith A Ellis

Cesar Martinez-Spessot

Marcin Spoczynski

Benjamin Walker

Victor Bayon-Molino

Atul Kwatra

Connor Upton

Weiqiang Ma

Arun Raghunath

Ian F Adams

John Mangan

Grigorios Katsaros

Chris Woods

Michael Christopher Woods

Sharon Ruane

Perumal Kappuudaiyar

Veeraraghavan Ramamurthy

Mohammad Chowdhury

Kevin Mullery

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.

Computational storage in a function-as-a-service architecture

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.

Intent-based cluster administration

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.

Intent-based orchestration in heterogenous compute platforms

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

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.

Methods and apparatus to control processing of telemetry data at an edge platform

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

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.

Method for distributing a computational process, workload distribution device and system for distributing a computational process

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.

Methods and apparatus to recommend instruction adaptations to improve compute performance

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

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

Growing community of inventors

Cologne, Germany

Thijs Metsch