公开(公告)号：US11863390B1

公开(公告)日：2024-01-02

申请号：US17888999

申请日：2022-08-16

Applicant: Nvidia Corporation

Inventor： Miriam Menes ,  Eitan Zahavi ,  Gil Bloch ,  Ahmad Atamli ,  Meni Orenbach ,  Mark Hummel ,  Glenn Dearth

IPC: G06F15/177 ,  H04L41/0873 ,  H04L45/488

CPC classification number: H04L41/0873 ,  H04L45/488

Abstract: Apparatuses, systems, and techniques are presented to configure computing resources to perform various tasks. In at least one embodiment, an approach presented herein can be used to verify whether a network of computing nodes is properly configured based, at least in part, on one or more expected data strings generated by the network of computing nodes.

公开(公告)号：US20240406058A1

公开(公告)日：2024-12-05

申请号：US18629132

申请日：2024-04-08

Applicant: Nvidia Corporation

Inventor： Elad Alon ,  Eitan Zahavi ,  Gaby Diengott ,  Shie Mannor ,  Vadim Gechman

IPC: H04L41/0659 ,  H04L41/147 ,  H04L43/06 ,  H04L43/0811

Abstract: A network monitor may execute, or communicate with, one or more stored machine learning models that are trained to predict a failure probability for one or more ports and/or links within a network fabric. Systems and methods may monitor a set of ports and/or links to generate predictions for failure probabilities using a first trained model and low frequency telemetry data. For a subset of ports and/or links with failure probabilities exceeding a first threshold, high speed telemetry data may be used by a second trained model to generate predictions for failure probabilities for the subset of ports. Suspicious ports may then be isolated and undergo various remediation and/or monitoring actions prior to de-isolating the isolated ports.

公开(公告)号：US12206748B2

公开(公告)日：2025-01-21

申请号：US17958139

申请日：2022-09-30

Applicant: NVIDIA Corporation

Inventor： Siddha Ganju ,  Elad Mentovich ,  Michael Balint ,  Eitan Zahavi ,  Michael Sabotta ,  Michael Norman ,  Ryan Wells

IPC: G06F11/30 ,  H04L41/16 ,  H04L67/60

Abstract: A method includes receiving, using a processing device, a first condition associated with an operation at a data center, where the operation at the data center pertains to a first location at the data center, the first location corresponding to a first parameter value. The method further includes providing the first condition as an input to a machine learning model. The method also includes performing one or more reinforcement learning techniques using the machine learning model to cause the machine learning model to output an indication of a final location associated with the operation, where the final location corresponds to a final parameter value that is closer to a target than the first parameter value corresponding to the first location at the data center.

公开(公告)号：US20240129380A1

公开(公告)日：2024-04-18

申请号：US17958139

申请日：2022-09-30

Applicant: NVIDIA Corporation

Inventor： Siddha Ganju ,  Elad Mentovich ,  Michael Balint ,  Eitan Zahavi ,  Michael Sabotta ,  Michael Norman ,  Ryan Wells

IPC: H04L67/60 ,  G06F11/30 ,  H04L41/16

CPC classification number: H04L67/60 ,  G06F11/3062 ,  H04L41/16

Abstract: A method includes receiving, using a processing device, a first condition associated with an operation at a data center, where the operation at the data center pertains to a first location at the data center, the first location corresponding to a first parameter value. The method further includes providing the first condition as an input to a machine learning model. The method also includes performing one or more reinforcement learning techniques using the machine learning model to cause the machine learning model to output an indication of a final location associated with the operation, where the final location corresponds to a final parameter value that is closer to a target than the first parameter value corresponding to the first location at the data center.