Hewlett-Packard Development Company, L.p.

Hewlett Packard Enterprise Development LP

Hewlett-Packard Company

Arm Limited

Hewlett-Packard Development, L.p.

Ludmila Cherkasova

Wenting Tang

Robert David Gardner

Jerome Rolia

Timothy Wood

Diwaker Gupta

Abhishek Verma

Qi Zhang

Yun Fu

Mehmet Kivanc Ozonat

Clifford A McCarthy

Daniel Juergen Gmach

Bernhard Kappler

Roger K T Lau

Guy Mathews

Wayne Greene

Lance Warren Russell

Arif Abdulhusein Merchant

James Edward Myers

Parameshwarappa Anand Kumar Savanth

Peter Phaal

Harald Burose

Julie Anna Symons

Ningfang Mi

Zhuoyao Zhang

Sharad Singhal

Mark David Lillibridge

Mbou Eyole

Kimberly Keeton

Jerome Alexander Rolia

Vinay K Deolalikar

Eric A Anderson

Xin Zhang

Alistair Veitch

Richard J Carter

Xiaozhou Li

Magnus Karlsson

Brent Arthur Enck

Charles B Morrey, Iii

Sahan Sajeewa Hiniduma Udugama Gamage

Craig A Soules

Richard J Friedrich

Tai Y Jin

Tomas G Rokicki

Martin F Arlitt

Vijay S Kumar

Loren Staley

Shankar Ravindra Ponnekanti

Gianfranco Ciardo

Subramaniam Venkata Kaiambur

Disclosed are methods, systems and devices for varying operations of a transponder device based, at least in part, on an availability of energy and/or power that may be harvested and/or collected. In one particular implementation, operations to generate one or more signals from sensor circuitry and/or to perform computations may be varied based, at least in part, on an availability of harvestable and/or collectable energy and/or power.

Device and/or method for adaptive computation

Disclosed are methods, systems and devices for allocating a power signal. In one particular implementation, a reader device may exchange messages with one more transponder devices to determine an allocation of a power signal. For example, one or more transponder devices may provide one or more messages in a downlink signal indicative of a requested signal up time.

Protocol for allocation of power signal

A method, system, and computer-readable storage device for selecting a heterogeneous platform configuration is described herein. In one aspect, a heterogeneous evaluation system may receive a platform configuration request from the tenant system. The platform configuration request can specify a job list and a quality of service metric. The heterogeneous evaluation system may then generate a heterogeneous platform configuration selection based on simulations of sub-lists formed from the job list. The heterogeneous platform configuration selection can specify a first platform configuration for a first duster and a second platform configuration for a second duster. The heterogeneous evaluation system can communicate the heterogeneous platform configuration selection to the tenant system.

Heterogeneous platform configurations

A method for tuning workflow settings in a distributed computing workflow comprising sequential interdependent jobs includes pairing a terminal stage of a first job and a leading stage of a second, sequential job to form an optimization pair, in which data segments output by the terminal stage of the first job comprises data input for the leading stage of the second job. The performance of the optimization pair is tuned by determining, with a computational processor, an estimated minimum execution time for the optimization pair and increasing the minimum execution time to generate an increased execution time. The method further includes calculating a minimum number of data segments that still permit execution of the optimization pair within the increased execution time.

Optimizing execution and resource usage in large scale computing

A method executes a simulation to determine backup times to backup objects to storage devices using a number of concurrent disk agents that are assigned to each of the storage devices. The number of concurrent disk agents is changed during the backup of the objects to the storage devices.

Changing a number of disk agents to backup objects to a storage device

To model a computing entity, information relating to transactions associated with the computing entity is received. The received information forms a collection of information. The collection is segmented into a plurality of segments, and at least one anomalous segment is identified. A model of the computing entity is built.

Modeling a computing entity

A job profile includes characteristics of a job to be executed, where the characteristics of the job profile relate to map tasks and reduce tasks of the job, and where the map tasks produce intermediate results based on input data, and the reduce tasks produce an output based on the intermediate results. In response to a failure in a system, numbers of failed map tasks and reduce tasks of the job based on a time of the failure are computed, and numbers of remaining map tasks and reduce tasks are computed. A performance model is provided, and a performance parameter of the job is estimated using the performance model.

Estimating a performance parameter of a job having map and reduce tasks after a failure

One embodiment collects performance data for an application server that processes transactions received from a client computer to a database server. An application log is created from the performance data and used for capacity planning in a multi-tiered architecture.

Capacity planning of multi-tiered applications from application logs

A job profile is received that includes characteristics of a job to be executed, where the characteristics of the job profile relate to map tasks and reduce tasks of the job. The map tasks produce intermediate results based on input data, and the reduce tasks produce an output based on the intermediate results. The characteristics of the job profile include at least one particular characteristic that varies according to a size of data to be processed. The at least one particular characteristic of the job profile is set based on the size of the data to be processed.

Varying a characteristic of a job profile relating to map and reduce tasks according to a data size

Determining a schedule of a batch workload of MapReduce jobs is disclosed. A set of multi-stage jobs for processing in a MapReduce framework is received, for example, in a master node. Each multi-stage job includes a duration attribute, and each duration attribute includes a stage duration and a stage type. The MapReduce framework is separated into a plurality of resource pools. The multi-stage jobs are separated into a plurality of subgroups corresponding with the plurality of pools. Each subgroup is configured for concurrent processing in the MapReduce framework. The multi-stage jobs in each of the plurality of subgroups are placed in an order according to increasing stage duration. For each pool, the multi-stage jobs in increasing order of stage duration are sequentially assigned from either a front of the schedule or a tail of the schedule by stage type.

Scheduling mapreduce job sets

A method comprises receiving, by a workload profiler, a representative workload of a computing system under analysis. The workload profiler determines a workload profile of the computing system that reflects a transaction mix that varies over time. A capacity analyzer receives the workload profile, and determines a maximum capacity of the computing system under analysis for serving the workload profile while satisfying a defined quality of service (QoS) target.

Capacity planning for computing systems hosting multi-tier application based on think time value and resource cost of composite transaction using statistical regression analysis

A system comprises a workload manager evaluator operable to receive a representative workload that is representative of competing workloads that share access to at least one shared computing resource. The workload manager evaluator is operable to evaluate performance of a scheduler that schedules access of the competing workloads to the shared computing resource according to defined control parameter values, wherein the workload manager evaluator evaluates performance of the scheduler under the representative workload for a plurality of different values of the control parameters. In certain embodiments, the workload manager evaluator determines an optimal value for the control parameters of the scheduler for scheduling access to the at least one shared computing resource for the representative workload to satisfy defined performance desires of the system.

Evaluating performance of workload manager based on QoS to representative workload and usage efficiency of shared resource for plurality of minCPU and maxCPU allocation values

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.

Evaluation of cloud computing services

A method comprises receiving a pattern of resource demands in a workload trace. The method further comprises identifying a plurality of occurrences of the determined pattern in the workload trace, and analyzing the occurrences to determine a trend of the workload trace. The method further comprises generating at least one synthetic workload trace representative of expected resource demands of the received workload trace accounting for the determined trend.

System and method for generating synthetic workload traces

Growing community of inventors

Sunnyvale, CA, United States of America

Ludmila Cherkasova