Hewlett-packard Development Company, L.p.

Hewlett Packard Enterprise Development LP

Micro Focus Lcc

Daniel Juergen Gmach

Yuan Chen

Cullen Edwin Bash

Dejan S Milojicic

Chris Deon Hyser

Jerome Alexander Rolia

Zhikui Wang

Ludmila Cherkasova

Martin Arlitt

Vanish Talwar

Amip J Shah

Thomas W Christian

Jerome Rolia

Sergey Blagodurov

Chandrakant D Patel

Ratnesh Kumar Sharma

Paolo Faraboschi

Douglas L Voigt

Manish Marwah

Anurag Singla

Alan Lynn Davis

Alvin AuYoung

Christopher Edward Hoover

Mustazirul Islam

Robert Block

Yupu Zhang

Umesh Markandaya Lakshminarasimha

Al Davis

Suranjan Pramanik

Sm Prakash Shiva

Zhenhua Liu

Donald E Bollinger

Yuvraj Patel

Suryanarayana Vijay

Julian James Stephen

Jayaram Kallapalayam Radhakrishnan

Adit Madan

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

An example system comprises one or more processing nodes to execute one or more processes; a switching fabric coupled to the one or more processing nodes; a fabric-attached memory (FAM) coupled with the switching fabric; and a memory allocator to allocate and release memory in the FAM in response to memory allocation requests and memory release requests from the one or more processes. The memory allocator is to partition the FAM into a memory shelf comprising a plurality of memory books of equal size. The memory allocator is to map a shelf into a virtual memory zone, the zone aligned with the boundaries of one or more books. The memory allocator is to maintain an indexed free-memory list where each index level is an entry point to a list of free memory blocks of a particular size in the zone, and the memory allocator to maintain a bitmap of the zone to identify if a memory block of a particular size is allocated.

Shared fabric attached memory allocator

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

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

In response to determining that an event matches a condition of a rule, a given one of a plurality of computing nodes is selected to send the event, based on one or both of an attribute of the event and an identifier of the rule. Information of the event is sent to the given computing node to perform correlation of the event with another event.

Selecting from computing nodes for correlating events

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 providing hardware assisted software versioning for clustered applications. In one example, virtualized global memory is accessible to application servers that provide a clustered application, where the clustered application includes multiple versions of a common data structure. After one of the application servers stores an element that is compatible with one version of the common data structure, other versions of the common data structure are located in the virtualized global memory. The element is then invalidated in the other versions of the common data structure to prevent access and translated directly in the virtualized global memory to the other versions of the common data structure. At this stage, the element can be validated in the other versions of the common data structure for access.

Hardware assisted software versioning of clustered applications

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 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

Managing computer resources. Workload state information associated with one or more servers in a data center is accessed by a computer, the workload state information comprises workload placement on one or more servers, cooling conditions of the one or more servers, and performance requirements associated with a data center. Cooling state information of cooling resources associated with the one or more servers are accessed by the computer, the cooling state information comprises cooling conditions of the cooling resources. The workload state information and the cooling state information are compared by the computer. Based on the comparing, the computer generates workload repositioning instructions that instruct repositioning of workload on the one or more servers to meet the performance requirements.

Managing computer resources

At least one embodiment is for a method for estimating resource costs required to process an workload to be completed using at least two different cloud computing models. Historical trace data of at least one completed workload that is similar to the workload to be completed is received by the computer. The processing of the completed workload is simulated using a t-shirt cloud computing model and a time-sharing model. The t-shirt and time-sharing resource costs are estimated based on their respective simulations. The t-shirt and resource costs are then compared.

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Palo Alto, CA, United States of America

Daniel Juergen Gmach