公开(公告)号：US20240386035A1

公开(公告)日：2024-11-21

申请号：US18785506

申请日：2024-07-26

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Mantej Singh Gill ,  Madhusoodhana Chari Sesha ,  Dhamodhran Sathyanarayanamurthy ,  Anil Babulal

IPC: G06F16/28 ,  G06N20/00

Abstract: Systems and methods are provided for receiving a time series dataset from a monitored processor and group the dataset into a plurality of clusters. Using an unsupervised machine learning model, the system may combine a subset of the plurality of clusters by data signature similarities to form a plurality of motifs and combine the plurality of motifs into one or more shapelets. In some examples, the system may train a supervised machine learning model using the plurality of motifs and the one or more shapelets as input to the supervised machine learning model. The system can perform various actions in response to labelling the time series dataset, including predicting a second time series dataset, determining that a monitored processor corresponds with an overutilization at a particular time, or suggesting a reduction of additional utilization of the monitored processor.

公开(公告)号：US12131146B2

公开(公告)日：2024-10-29

申请号：US18146096

申请日：2022-12-23

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Mantej Singh Gill ,  Dhamodhran Sathyanarayanamurthy ,  Arun Mahendran

IPC: G06F8/65

CPC classification number: G06F8/65

Abstract: A device and corresponding method are provided to provide accurate estimates of how long it will take to install updates to compute nodes in a large-scale computer deployment. a duration prediction model is trained using historical data from previous updates to compute nodes. The features selected to train the duration prediction model are update features including update component type, update component size, update component duration and compute node features including operating system, BMC type/version, UEFI type/version, and generation for each of the compute nodes updated. The historical data for the features is accessed from a metadata store.

公开(公告)号：US20240302878A1

公开(公告)日：2024-09-12

申请号：US18182088

申请日：2023-03-10

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Mantej Singh Gill ,  Dhamodhran Sathyanarayanamurthy ,  Madhusoodhana Chari Sesha ,  Arun Mahendran

IPC: G06F1/18 ,  G06F1/3228

CPC classification number: G06F1/189 ,  G06F1/3228

Abstract: Systems and methods are provided for compressing a time-series dataset from a monitored device into a compressed dataset representation. Using an unsupervised machine learning model, the system may group a contiguous set of datapoints of the time-series dataset and group, using a distance algorithm, the first cluster to a first motif. Many motifs can be generated to identify different data signatures in the time-series dataset. The plurality of motifs can be used to generate a data definition, motif sequence graph, directed graph, or other combinations of datapoints. These datapoints can be combined through a summation process with other datapoints generated by a second machine learning model. The output of the summation process can be used to forecast device usage of a monitored device in a data center.

公开(公告)号：US12086000B1

公开(公告)日：2024-09-10

申请号：US18182088

申请日：2023-03-10

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Mantej Singh Gill ,  Dhamodhran Sathyanarayanamurthy ,  Madhusoodhana Chari Sesha ,  Arun Mahendran

IPC: G06F1/18 ,  G06F1/3228

CPC classification number: G06F1/189 ,  G06F1/3228

Abstract: Systems and methods are provided for compressing a time-series dataset from a monitored device into a compressed dataset representation. Using an unsupervised machine learning model, the system may group a contiguous set of datapoints of the time-series dataset and group, using a distance algorithm, the first cluster to a first motif. Many motifs can be generated to identify different data signatures in the time-series dataset. The plurality of motifs can be used to generate a data definition, motif sequence graph, directed graph, or other combinations of datapoints. These datapoints can be combined through a summation process with other datapoints generated by a second machine learning model. The output of the summation process can be used to forecast device usage of a monitored device in a data center.

公开(公告)号：US12050626B2

公开(公告)日：2024-07-30

申请号：US17991500

申请日：2022-11-21

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Mantej Singh Gill ,  Madhusoodhana Chari Sesha ,  Dhamodhran Sathyanarayanamurthy ,  Anil Babulal

IPC: G06F16/28 ,  G06N20/00

CPC classification number: G06F16/285 ,  G06N20/00

Abstract: Systems and methods are provided for receiving a time series dataset from a monitored processor and group the dataset into a plurality of clusters. Using an unsupervised machine learning model, the system may combine a subset of the plurality of clusters by data signature similarities to form a plurality of motifs and combine the plurality of motifs into one or more shapelets. In some examples, the system may train a supervised machine learning model using the plurality of motifs and the one or more shapelets as input to the supervised machine learning model. The system can perform various actions in response to labelling the time series dataset, including predicting a second time series dataset, determining that a monitored processor corresponds with an overutilization at a particular time, or suggesting a reduction of additional utilization of the monitored processor.