Hewlett Packard Enterprise Development LP

Hewlett-packard Development Company, L.p.

Micro Focus Lcc

Yuan Chen

Daniel Juergen Gmach

Cullen Edwin Bash

Dejan S Milojicic

Vanish Talwar

Chris Deon Hyser

Zhikui Wang

Martin Arlitt

Thomas W Christian

Amip J Shah

Jerome Alexander Rolia

Subramoniam N Iyer

Zhenhua Liu

Sergey Blagodurov

Chandrakant D Patel

Ratnesh Kumar Sharma

Paolo Faraboschi

Manish Marwah

Alan Andrew McReynolds

Xue Liu

Alan Lynn Davis

Christopher Edward Hoover

Akhil Sahai

Indrajit Roy

Niru Kumari

Mustazirul Islam

Umesh Markandaya Lakshminarasimha

Al Davis

Sm Prakash Shiva

Naila Farooqui

Daniel Jurgen Gmach

Dazhao Cheng

Adit Madan

Suryanarayana Vijay

Jack Yanxin Li

Chetan Shankar

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

In one example, a memory network may control access to a shared memory that is by multiple compute nodes. The memory network may control the access to the shared memory by receiving a memory access request originating from an application executing on the multiple compute nodes and determining a priority for processing the memory access request. The priority determined by the memory network may correspond to a memory address range in the memory that is specifically used by the application.

Memory network to prioritize processing of a memory access request

Example implementations relate to scaling a processing system. An example implementation includes receiving an application having a number of operators for performing a service in the processing system. A metric of the processing system may be monitored while the application runs, and the processing system may be scaled where the metric surpasses a threshold. In an example, the processing system may be scaled by increasing or decreasing the number of operators of the application.

Scaling processing systems

A system management command is stored in a management partition of a global memory by a first node of a multi-node computing system. The global memory is shared by each node of the multi-node computing system. In response to an indication to access the management partition, the system management command is accessed from the management partition by a second node of the multi-node computing system. The system management command is executed by the second node. Executing the system management command includes managing the second node.

Computing system management using shared memory

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, an instruction to run a kernel of an application on an apparatus having a first processing unit integrated with a second processing unit may be received. In addition, an application profile for the application at a runtime of the application kernel on the second processing unit may be created, in which the application profile identifies an affinity of the application kernel to be run on either the first processing unit or the second processing unit, and identifies a characterization of an input data set of the application. The application profile may also be stored in a data store.

Integrated heterogeneous processing units

A system comprises a plurality of functional units powered via a power source. The system further comprises a first functional unit and a second functional unit, wherein the second functional unit is to promote the first functional unit to a management unit based on a management requirement of the system. The management unit is to administrate operations of the system. Once the first functional unit is promoted, the management unit is isolated from the functional units that were not promoted via a virtual network path and a power management unit.

Functional unit promotion to management unit

Examples relate to managing data processing resources. In one example, a computing device may: determine, for each of a plurality of data processing jobs, that the job is independent or dependent; allocate data processing resources to an independent job processing pool or a dependent job processing pool based on an initial resource share value indicating how resources are to be allocated between job processing pools; determine a first policy for scheduling data to be processed by processing resources allocated to the independent job processing pool; determine a second policy for scheduling data to be processed by processing resources allocated to the dependent job processing pool; determine an initial parallelism value that specifies a number of concurrently processing jobs; and provide a processing device with instructions to process batches of data using the allocation of data processing resources, the first policy, the second policy, and the initial parallelism value.

Managing data processing resources

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

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 power management may include collecting data related to resource utilization and process information from a server. The method may further include using the collected data to generate use and state models respectively based on use and state categories of the server. The method may also include determining a state condition of the server through use of the generated use and state models to manage power utilization of the server.

Use and state based power management

According to an example, an application performance measurement for an application for a current time interval, a performance specification for the application, and a resource consumption metric for a resource of a plurality of resources that are to process the application for the current time interval may be accessed. In addition, the application performance measurement, the performance specification, and the resource consumption metric may be used to determine a resource specification for a next time interval for the resource of the plurality of resources. Moreover, the resource specification may be used to determine, by a processor, a resource weight for the resource of the plurality of resources for the next time interval.

Weight-based collocation management

A method of maximizing server utilization within a datacenter comprising collocating a number of workloads to a subset of a number of physical servers within a datacenter, adjusting access to resources by critical and non-critical workloads, isolating a number of critical workloads that share resources to the subset of resource instances, and controlling resource utilization of the collocated workloads. A system for collocating workloads in a datacenter, the system comprising a number of servers communicatively coupled to each other and a collocation module that receives information from each server and decides whether to leave a workload running on one of any of the servers on that server, or migrate a workload running on one of the number of servers to another server.

Maximizing server utilization within a datacenter

A data center workload distribution management system includes a cooling cost engine to determine a cooling cost or cooling capacity for each of a plurality of zones of a data center and a workload distribution engine. The workload distribution engine is to identify the zone that has a lowest cooling cost and sufficient cooling capacity and also has sufficient processing capacity for a workload, determine a local cooling efficiency index for at least one location within the identified zone, and distribute the workload to the location having a local cooling efficiency index that indicates the highest cooling efficiency.

Systems and methods for distributing a workload based on a local cooling efficiency index determined for at least one location within a zone in a data center

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Sunnyvale, CA, United States of America

Yuan Chen