公开(公告)号：US20230342398A1

公开(公告)日：2023-10-26

申请号：US17727230

申请日：2022-04-22

Applicant: VMware, Inc.

Inventor： Shay Vargaftik ,  Yaniv Ben-Itzhak ,  Alex Markuze ,  Igor Golikov ,  Avishay Yanai

IPC: G06F16/903

CPC classification number: G06F16/90335

Abstract: Some embodiments provide a method for using a machine learning (ML) model to respond to a query, at a smart NIC of a computer. The method receives a query including an input. The method applies a first ML model to the input to generate an output and a confidence measure for the output. When the confidence measure for the output is below a threshold, the method discards the output and provides the query to the computer for the computer to apply a second ML model to the input.

公开(公告)号：US11579908B2

公开(公告)日：2023-02-14

申请号：US16223235

申请日：2018-12-18

Applicant: VMware, Inc.

Inventor： Aditi Ghag ,  Pranshu Jain ,  Yaniv Ben-Itzhak ,  Jianjun Shen

IPC: G06F9/455 ,  G06F9/48 ,  G06F9/50 ,  H04L43/065 ,  H04L43/0876

Abstract: A method for containerized workload scheduling can include determining a network state for a first hypervisor in a virtual computing cluster (VCC). The method can further include determining a network state for a second hypervisor. Containerized workload scheduling can further include deploying a container to run a containerized workload on a virtual computing instance (VCI) deployed on the first hypervisor or the second hypervisor based, at least in part, on the determined network state for the first hypervisor and the second hypervisor.

公开(公告)号：US20220180244A1

公开(公告)日：2022-06-09

申请号：US17115432

申请日：2020-12-08

Applicant: VMware, Inc.

Inventor： Yaniv Ben-Itzhak ,  Shay Vargaftik

IPC: G06N20/00 ,  G06F16/23 ,  G06N5/04

Abstract: In one set of embodiments, a computer system can receive an unlabeled dataset comprising a plurality of unlabeled data instances, each unlabeled data instance including values for a plurality of features. The computer system can train, for each feature, a supervised machine learning (ML) model on a labeled dataset derived from the unlabeled dataset, where the labeled dataset comprises a plurality of labeled data instances, and wherein each labeled data instance includes (1) a label corresponding to a value for the feature in an unlabeled data instance of the unlabeled dataset, and (2) values for other features in the unlabeled data instance. The computer system can then compute, for each pair of first and second features in the plurality of features, an inter-feature influence score using the trained supervised ML model for the second feature, the inter-feature influence score indicating how useful the first feature is in predicting the second feature.

公开(公告)号：US20220012626A1

公开(公告)日：2022-01-13

申请号：US16924048

申请日：2020-07-08

Applicant: VMware, Inc.

Inventor： Yaniv Ben-Itzhak ,  Shay Vargaftik

IPC: G06N20/00 ,  G06K9/62

Abstract: Techniques for implementing unsupervised anomaly detection by self-prediction are provided. In one set of embodiments, a computer system can receive an unlabeled training data set comprising a plurality of unlabeled data instances, where each unlabeled data instance includes values for a plurality of features. The computer system can further train, for each feature in the plurality of features, a supervised machine learning (ML) model using a labeled training data set derived from the unlabeled training data set, receive a query data instance, and generate a self-prediction vector using at least a portion of the trained supervised ML models and the query data instance, where the self-prediction vector indicates what the query data instance should look like if it were normal. The computer system can then generate an anomaly score for the query data instance based on the self-prediction vector and the query data instance.

公开(公告)号：US20220012550A1

公开(公告)日：2022-01-13

申请号：US16923988

申请日：2020-07-08

Applicant: VMware, Inc.

Inventor： Yaniv Ben-Itzhak ,  Shay Vargaftik

IPC: G06K9/62 ,  G06F9/50 ,  G06N20/20

Abstract: Techniques for implementing a tree-based ensemble classifier comprising an internal load balancer are provided. In one set of embodiments, the tree-based ensemble classifier can receive a query data instance and select, via the internal load balancer, a subset of its decision trees for processing the query data instance. The tree-based ensemble classifier can then query each decision tree in the selected subset with the query data instance, combine the per-tree classifications generated by the subset trees to generate a subset classification, and determine whether a confidence level associated with the subset classification is sufficiently high. If the answer is yes, the tree-based ensemble classifier can output the subset classification as a final classification result for the query data instance. If the answer is no, the tree-based ensemble classifier can repeat the foregoing steps until a sufficient confidence level is reached or until all of its decision trees have been selected and queried.