公开(公告)号：US20220147430A1

公开(公告)日：2022-05-12

申请号：US17415766

申请日：2019-07-25

Applicant: Hewlett-Packard Development Company, L.P.

Inventor： Carlos Haas Costa ,  Christian Makaya ,  Madhu Sudan Athreya ,  Raphael Gay ,  Pedro Henrique Garcez Monteiro

IPC: G06F11/34 ,  G06N20/00

Abstract: For each of a number of workloads, time intervals within execution performance information that was collected during execution of the workload on a first hardware platform are correlated with corresponding time intervals within execution performance information that was collected during execution of the workload on a second hardware platform. For a workload, the time intervals within the execution performance information on the second hardware platform are correlated to the time intervals within the execution performance information the first hardware platform during which the same parts of the workload were executed. A machine learning model that outputs predicted performance on the second hardware platform relative to known performance on the first hardware platform is trained. The model is trained from the correlated time intervals within the execution performance information for each workload on the hardware platforms.

公开(公告)号：US20220197706A1

公开(公告)日：2022-06-23

申请号：US17418797

申请日：2019-09-11

Applicant: Hewlett-Packard Development Company, L.P.

Inventor： Jonathan Munir Salfity ,  Amalendu Kulthumani Iyer ,  Christian Makaya

IPC: G06F9/50 ,  G06F9/48

Abstract: Examples of scheduling of a cyber-physical system (CPS) process through a utility function are described. In an example, a plurality of computing resources to perform a process for the CPS may be evaluated based on a utility function. A first computing resource may be onboard the CPS and a second computing resource may be a remote computing resource. A computing resource that optimizes the utility function may be scheduled to perform the process for the CPS.

公开(公告)号：US20230168925A1

公开(公告)日：2023-06-01

申请号：US17920328

申请日：2020-04-23

Applicant: HEWLETT-PACKARD DEVELOPMENT COMPANY, L.P.

Inventor： Christian Makaya ,  Madhu Sudan Athreya

IPC: G06F9/48 ,  G06F9/30

CPC classification number: G06F9/4881 ,  G06F9/30036 ,  G06F9/3004

Abstract: Examples of computing task scheduling based on an intrusiveness metric are described. In an example, an intrusiveness metric that indicates an impact of a computing task on performance of a computing device may be determined with an intrusiveness machine learning model. The intrusiveness metric may be sent to a scheduler device to determine distribution of additional computing tasks according to a scheduling machine learning model.

公开(公告)号：US20230012487A1

公开(公告)日：2023-01-19

申请号：US17783311

申请日：2019-12-20

Applicant: Hewlett-Packard Development Company, L.P.

Inventor： Christian Makaya ,  Madhu Athreya ,  Carlos Haas Costa

IPC: G06F9/50

Abstract: Systems and methods are described herein to orchestrate the execution of an application, such as a machine learning or artificial intelligence application, using distributed compute clusters with heterogeneous compute resources. A discovery subsystem may identify the different compute resources of each compute cluster. The application is divided into a plurality of workloads with each workload associated with resource demands corresponding to the compute resources of one of the compute clusters. Adaptive modeling allows for hyperparameters to be defined for each workload based on the compute resources associated with the compute cluster to which each respective workload is assigned and the associated dataset.

公开(公告)号：US20220353193A1

公开(公告)日：2022-11-03

申请号：US17765877

申请日：2019-10-18

Applicant: Hewlett-Packard Development Company, L.P.

Inventor： Amalendu Iyer ,  Christian Makaya ,  Jonathan Munir Salfity

IPC: H04L47/263 ,  H04W28/10

Abstract: According to examples, an apparatus may include a processor and a non-transitory computer readable medium on which is stored instructions that the processor may execute to determine whether to modify a transmission rate at which time series data is transmitted to a remote computer. The determination is based on a prediction of the time series data.