Hewlett-packard Development Company, L.p.

Hewlett Packard Enterprise Development LP

Hewlett-packard Company

Micro Focus Lcc

Hp Development Company L.l.p.

Cullen Edwin Bash

Chandrakant D Patel

Ratnesh Kumar Sharma

Abdlmonem H Beitelmal

Amip J Shah

Manish Marwah

Martin Arlitt

Zhikui Wang

Yuan Chen

Daniel Juergen Gmach

Thomas W Christian

Parthasarathy Ranganathan

Geoff M Lyon

Christopher Gregory Malone

Chris Deon Hyser

Keith Farkas

David Allen Moore

Tahir Cader

George H Forman

Dejan S Milojicic

Alan Andrew McReynolds

Glenn Cochran Simon

Gopalakrishnan Janakiraman

Jichuan Chang

Jerome Alexander Rolia

Christopher Edward Hoover

Martha Lurie Lyons

Umeshwar Dayal

Ming C Hao

Kimberly Keeton

Niraj Tolia

John P Franz

Meichun Hsu

Paolo Faraboschi

Salil Pradhan

Siamak Tavallaei

Chih C Shih

Niru Kumari

Gowtham Bellala

Rongliang Zhou

Carlos J Felix

Shailesh N Joshi

Robert M Tozer

Justin James Meza

Sergio Escobar-Vargas

Abdimonem H Beitelmal

Zhenhua Liu

Bret Alan McKee

Sven Graupner

Xiaoyun Zhu

Daniel Keim

Cyril Brignone

Osamu Samuel Nakagawa

Richard J Friedrich

Robert Allen Pereira

Michael Hund

Henry C Coles

Sebastian Mittelstaedt

Andrew James Phelan

Mustazirul Islam

Sm Prakash Shiva

William J Navas

Sergey Blagodurov

Sabastian Mittelstadt

Carlos E Ramirez

Sarah Anthony

Chris G Malone

Scot Austin

Daniel Jurgen Gmach

Edward M Aoki

Richard Shaw Kaufmann

Hyungsul Kim

Abdimonem Beitelal

Ernesto J Ferrer

Marcos A Batista

Christian J Bonilla

Chandrakani Patel

Maniel Sotomayor

Robert Tozer

Abdlmonem Beitelal

Systems and methods are provided for incorporating an optimized dispatcher with an FaaS infrastructure to permit and restrict access to resources. For example, the dispatcher may assign requests to 'warm' resources and initiate a fault process if the resource is overloaded or a cache-miss is identified (e.g., by restarting or rebooting the resource). The warm instances or accelerators associated with the allocation size that are identified may be commensurate to the demand and help dynamically route requests to faster accelerators.

Optimal dispatching of Function-as-a-Service in heterogeneous accelerator environments

Optimal dispatching of function-as-a-service in heterogeneous accelerator environments

Quantifying power usage for a service. An example method may include identifying a dependency model for the service, the dependency model based in part on infrastructure providing the service. The method may also include determining power usage for the service using the dependency model.

Quantifying power usage for a service

The present disclosure includes a system and method for apportioning power consumption. In an example of apportioning power consumption according to the present disclosure, a transaction mix for a service is determined (), component resource usage for each of a number of components that are used while completing the service is determined (), and component power consumption for each of the number of components is determined by use of the component resource usage ().

Systems and methods for apportioning power consumption

According to an example, a method for area occupancy information extraction may include receiving a signal from a sensor, and determining a number of occupants moving within a first area based on the signal. The method may further include determining when an occupant is moving and a direction of motion of the occupant based on the signal, and determining a most likely trajectory based on the direction of motion of the occupant. The method may also include controlling, by a processor, a service in the first area based on the determination of the number of occupants, the determination of when the occupant is moving and the direction of motion of the occupant, and/or the determination of the most likely trajectory.

Area occupancy information extraction

A model is provided that produces predicted sensor data as a function of at least one input feature that includes an adjustable setting of a cooling infrastructure. The model is able to model a non-linear relationship between the predicted sensor data and the adjustable setting.

Providing a model of impact of a cooling infrastructure

Systems and methods for operating based on recovered waste heat are described. In one example, the method includes receiving recovered waste heat power and operating at least one system component of a recovered waste heat based computing device based on the recovered waste heat power, where the at least one system component is coupled to a non-volatile memory of the recovered waste heat based computing device. The method further includes preserving operational states of the at least one system component in the non-volatile memory based on a current power level associated with the recovered waste heat power.

Computing devices operable on recovered waste heat

In an implementation, a method for managing airflow distribution through a plurality of adaptive vent tiles comprises accessing a model that correlates an environmental condition with a setting arrangement of the plurality of adaptive vent tiles and operational settings of a plurality of fluid moving devices. The method also includes calculating, through implementation of the model, a setting arrangement of the plurality of adaptive vent tiles that results in a substantially optimized metric subject to a set of constraints, wherein the setting arrangement of the plurality of adaptive vent tiles comprises an arrangement in which adaptive vent tiles in respective groups of adaptive vent tiles have substantially uniform opening levels with respect to each other.

Managing airflow distribution through adaptive vent tiles

An example device in accordance with an aspect of the present disclosure includes a substrate that may be disposed in a housing. The substrate includes a sprayer to spray coolant.

Substrate sprayer

In a method for generating a capacity schedule for a facility to meet a predefined operational goal, in which the facility is to receive resources from a first resource supply, a prediction of a supply of resources available from the first resource supply for a predetermined period of time is accessed. In addition, a prediction of a demand for resources in the facility during the predetermined period of time is accessed. The prediction of the supply of resources and the prediction of the demand for resources are inputted into a constrained optimization problem and the capacity schedule for the facility is generated by finding a solution to the constrained optimization problem.

Generating a capacity schedule for a facility

According to an example, a cooling system includes a cooling fluid reservoir, a cooling apparatus in fluid communication with the cooling fluid reservoir, a chamber having a side in thermal contact with a portion of the heat generating component, in which cooling fluid delivered by the cooling apparatus is to be heated through receipt of heat from the heat generating component, a cooling plate positioned at a distance and separated from the chamber, and a cooling fluid tube connecting the chamber and the cooling plate, in which the heated cooling fluid is to flow through the cooling fluid tube to the cooling plate. The cooling plate is also to be in thermal contact with a heat exchanger that that is to remove heat from the cooling fluid.

Electronic apparatus having a cooling apparatus

Resource management for data centers is disclosed. In an exemplary embodiment, a method includes determining electrical power usage for the data center, and determining cooling fluid usage for the data center. The method also includes processing a resource utilization cap for the data center, and adjust ng at least one of the electrical power and the cooling fluid for the data center based on the resource utilization cap.

Resource management for data centers

A demand response for an energy-consuming facility is disclosed. A demand response is generated by estimating a likelihood of a coincident peak time period, modeling workloads to be scheduled in the energy-consuming facility, determining a workload schedule based on the likelihood of the coincident peak time period and a plurality of utility charging rates, and scheduling the workloads for execution in the energy-consuming facility according to the determined workload schedule.

Generating a demand response for an energy-consuming facility

A method for determining regions of influence of a plurality of fluid moving devices in an infrastructure is provided. In the method, a plurality of clusters are generated, with each of the plurality of clusters containing a representation of fluid supply data pertaining to a fluid moving device and one or more of the plurality of sensors based upon collected sensor and fluid supply data. In addition, regions of influence of each of the plurality of fluid moving devices are identified from an analysis of the generated clusters.

Determining regions of influence of fluid moving devices

Growing community of inventors

Los Gatos, CA, United States of America

Cullen Edwin Bash