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    Self-adjustable end-to-end stack programming
    1.
    发明授权
    Self-adjustable end-to-end stack programming 有权

    公开(公告)号：US11182134B2

    公开(公告)日：2021-11-23

    申请号：US16799637

    申请日：2020-02-24

    Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

    Inventor： Dejan S. Milojicic ,  Mehmet Kivanc Ozonat ,  Sergey Serebryakov

    IPC: G06F8/35 ,  G06F9/445

    Abstract: Systems and methods are provided for optimizing parameters of a system across an entire stack, including algorithms layer, toolchain layer, execution or runtime layer, and hardware layer. Results from the layer-specific optimization functions of each domain can be consolidated using one or more consolidation optimization functions to consolidate the layer-specific optimization results, capturing the relationship between the different layers of the stack. Continuous monitoring of the programming model during execution may be implemented and can enable the programming model to self-adjust based on real-time performance metrics. In this way, programmers and system administrators are relieved of the need for domain knowledge and are offered a systematic way for continuous optimization (rather than an ad hoc approach).

    SELF-ADJUSTABLE END-TO-END STACK PROGRAMMING
    3.
    发明申请
    SELF-ADJUSTABLE END-TO-END STACK PROGRAMMING 有权

    公开(公告)号：US20210263713A1

    公开(公告)日：2021-08-26

    申请号：US16799637

    申请日：2020-02-24

    Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

    Inventor： Dejan S. Milojicic ,  Mehmet Kivanc Ozonat ,  Sergey Serebryakov

    IPC: G06F8/35 ,  G06F9/445

    Abstract: Systems and methods are provided for optimizing parameters of a system across an entire stack, including algorithms layer, toolchain layer, execution or runtime layer, and hardware layer. Results from the layer-specific optimization functions of each domain can be consolidated using one or more consolidation optimization functions to consolidate the layer-specific optimization results, capturing the relationship between the different layers of the stack. Continuous monitoring of the programming model during execution may be implemented and can enable the programming model to self-adjust based on real-time performance metrics. In this way, programmers and system administrators are relieved of the need for domain knowledge and are offered a systematic way for continuous optimization (rather than an ad hoc approach).

    CANDIDATE SERVICES FOR AN APPLICATION
    6.
    发明申请
    CANDIDATE SERVICES FOR AN APPLICATION 审中-公开
    申请的候选服务

    公开(公告)号：US20170017655A1

    公开(公告)日：2017-01-19

    申请号：US15114134

    申请日：2014-03-31

    Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

    Inventor： Jervis Pinto ,  Mehmet Kivanc Ozonat ,  William K. . ,  Mehmet Oguz Oguz ,  Alkiviadis Simitsis

    IPC: G06F17/30 ,  G06N5/04 ,  G06N99/00

    CPC classification number: G06F16/24578 ,  G06F8/33 ,  G06F8/34 ,  G06F16/248 ,  G06F16/951 ,  G06N5/04 ,  G06N20/00

    Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to provide candidate services for an application. An example method includes determining a plurality of candidate services for a cloud application, determining an indication that a first candidate service from the plurality of candidate services is more relevant to the cloud application than a second candidate service based on a first prediction score corresponding to the first candidate service and a second prediction score corresponding to the second candidate service; presenting the first candidate service and the second candidate service to a user based on the first prediction score and the second prediction score; and adjusting a first weight corresponding to the first candidate service and a second weight corresponding to the second candidate service based on whether the first candidate service or the second candidate service is selected for inclusion in the cloud application.

    Abstract translation: 公开了方法，装置，系统和制品以提供应用的候选服务。 一种示例性方法包括：确定用于云应用的多个候选服务，基于与第二候选服务相对应的第一预测分数，确定来自多个候选服务的第一候选服务与第二候选服务相比更适合于云应用的指示 第一候选服务和对应于第二候选服务的第二预测分数; 基于第一预测分数和第二预测分数向用户呈现第一候选服务和第二候选服务; 以及基于所述第一候选服务或所述第二候选服务是否被选择以包括在所述云应用中，来调整对应于所述第一候选服务的第一权重和对应于所述第二候选服务的第二权重。

    MACHINE LEARNING FOR MONITORING, MANAGING AND MAINTAINING EDGE DATA CENTERS
    7.
    发明申请
    MACHINE LEARNING FOR MONITORING, MANAGING AND MAINTAINING EDGE DATA CENTERS 有权

    公开(公告)号：US20210133369A1

    公开(公告)日：2021-05-06

    申请号：US16673243

    申请日：2019-11-04

    Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

    Inventor： Tahir Cader ,  Mehmet Kivanc Ozonat

    IPC: G06F30/20

    Abstract: In exemplary aspects of managing, monitoring and maintaining computing systems and devices such as edge data centers (EDCs), probabilistic models such as dynamic Bayesian networks (DBNs) are generated. The DBNs can define individual and collective systems such as EDCs. The DBNs are built by generating or estimating the model structure and model parameters. The model can be deployed, for instance, to identify actual or potentially anomalous behavior within the individual or collective systems defined by the model. The model can also be deployed to predict anomalous behavior. Based on the results of the model, corrective measures can be taken to remedy the anomalies, and/or to optimize the impact therefrom.

    Systems and methods for performing a fast simulation
    10.
    发明授权
    Systems and methods for performing a fast simulation 有权

    公开(公告)号：US10579754B1

    公开(公告)日：2020-03-03

    申请号：US16132332

    申请日：2018-09-14

    Applicant: Hewlett Packard Enterprise Development LP

    Inventor： Mehmet Kivanc Ozonat ,  Ablimit Aji ,  Mehmet Oguz Sayal ,  Natalia Vasileva

    IPC: G06F11/263 ,  G01R31/3183 ,  G06F17/50 ,  G01R31/319

    Abstract: Systems and methods are provided for performing a fast simulation using test parameter vectors as inputs. The method includes retrieving precomputed samples from a sample repository stored in a non-volatile memory, the precomputed samples being precomputed using a simulated model, predetermined parameter vectors, and random inputs; storing respective subsets of the precomputed samples in local memories of a plurality of respective hardware processors; storing the test parameter vectors in the local memories of the hardware processors; at each of the hardware processors, selecting a subset of the precomputed samples stored in the local memory of the hardware processor based on the test parameter vectors, computing test samples by executing the simulated model using the test parameter vectors and the random inputs; and combining the subset of the precomputed samples and the test samples to produce a simulation result.

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