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Patent Agency Ranking
公开(公告)号：US20230376855A1
公开(公告)日：2023-11-23
申请号：US18361511
申请日：2023-07-28
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Inventor： Klaus-Dieter Lange , Mukund Kumar , Prateek Bhatnagar , Nalamati Sai Rajesh , Nishant Rawtani , Craig Allan Estepp
IPC: G06N20/00 , G06F11/34 , G06F11/30 , G06F11/32 , G06F18/214
CPC classification number: G06N20/00 , G06F11/3428 , G06F11/3075 , G06F11/327 , G06F11/3006 , G06F18/214
Abstract: Systems and methods are provided for detecting anomalies on multiple layers of a computer system, such as a compute server. For example, the system can detect anomalies from the lower firmware layer up to the upper application layer of the compute server. The system collects train data from the computer system that is under testing. The train data includes features that affect performance metrics, as defined by a selected benchmark. This train data is used in training machine learning (ML) models. The ML models create a train snapshot corresponding to the selected benchmark. Additionally with every new release, a test snapshot can be created corresponding to the selected benchmark or workload. The system can detect an anomaly based on the train snapshot and the test snapshot. Also, the system can recommend tunings for a best set of features based upon data collected over generations of compute server.
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公开(公告)号：US20210365302A1
公开(公告)日：2021-11-25
申请号：US16878238
申请日：2020-05-19
Applicant: Hewlett Packard Enterprise Development LP
Abstract: An Adaptive and Distributed Tuning System (ADTS) includes a distributed framework for full-stack performance tuning of workloads. Given a large search space, the framework leverages domain-specific contextual information, in the form of probabilistic models of the system behavior, to make informed decisions about which configurations to evaluate and, in turn, distribute across multiple nodes to converge rapidly to best possible configurations.
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公开(公告)号：US11755955B2
公开(公告)日：2023-09-12
申请号：US17225897
申请日：2021-04-08
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Klaus-Dieter Lange , Mukund Kumar , Prateek Bhatnagar , Nalamati Sai Rajesh , Nishant Rawtani , Craig Allan Estepp
IPC: G06N20/00 , G06F11/30 , G06F11/32 , G06F11/34 , G06F18/214
CPC classification number: G06N20/00 , G06F11/3006 , G06F11/3075 , G06F11/327 , G06F11/3428 , G06F18/214
Abstract: Systems and methods are provided for detecting anomalies on multiple layers of a computer system, such as a compute server. For example, the system can detect anomalies from the lower firmware layer up to the upper application layer of the compute server. The system collects train data from the computer system that is under testing. The train data includes features that affect performance metrics, as defined by a selected benchmark. This train data is used in training machine learning (ML) models. The ML models create a train snapshot corresponding to the selected benchmark. Additionally with every new release, a test snapshot can be created corresponding to the selected benchmark or workload. The system can detect an anomaly based on the train snapshot and the test snapshot. Also, the system can recommend tunings for a best set of features based upon data collected over generations of compute server.
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公开(公告)号：US20220253689A1
公开(公告)日：2022-08-11
申请号：US17171282
申请日：2021-02-09
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Klaus-Dieter Lange , Mukund Kumar , Shreeharsha Gudal Neelakantachar , Hung D. Cao
Abstract: Predictive big data capacity planning is described. An example includes instructions for receiving workload data and computing operation data related to workload processing for a customer in a computing infrastructure, the computing infrastructure including one or more clusters, the one or more clusters including one or more data nodes; analyzing the received data to identify relationship information between the workload data and the computing operation data; performing predictive analytics to identify a significant value that relates to performance variations in workload performance or usage pattern characteristics for data growth scale factors in the computing infrastructure; generating a knowledge base based at least in part on the predictive analytics; training a machine learning model based at least in part on the knowledge base; and utilizing the trained machine learning model to generate a computing infrastructure configuration recommendation for the customer.
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公开(公告)号：US12086710B2
公开(公告)日：2024-09-10
申请号：US17171282
申请日：2021-02-09
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Klaus-Dieter Lange , Mukund Kumar , Shreeharsha Gudal Neelakantachar , Hung D Cao
IPC: G06N3/08 , G06F17/18 , G06F18/214 , G06F18/23 , G06N20/10
CPC classification number: G06N3/08 , G06F17/18 , G06F18/2155 , G06F18/23 , G06N20/10
Abstract: Predictive big data capacity planning is described. An example includes instructions for receiving workload data and computing operation data related to workload processing for a customer in a computing infrastructure, the computing infrastructure including one or more clusters, the one or more clusters including one or more data nodes; analyzing the received data to identify relationship information between the workload data and the computing operation data; performing predictive analytics to identify a significant value that relates to performance variations in workload performance or usage pattern characteristics for data growth scale factors in the computing infrastructure; generating a knowledge base based at least in part on the predictive analytics; training a machine learning model based at least in part on the knowledge base; and utilizing the trained machine learning model to generate a computing infrastructure configuration recommendation for the customer.
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