公开(公告)号：US20230262114A1

公开(公告)日：2023-08-17

申请号：US18307504

申请日：2023-04-26

Applicant: VMware, Inc.

Inventor： Alok TIAGI ,  Farzad GHANNADIAN ,  Karen HAYRAPETYAN ,  Laxmikant Vithal GUNDA ,  Sunitha KRISHNA ,  Ashot ASLANYAN ,  Anirban SENGUPTA

IPC: H04L67/1012 ,  H04L47/78 ,  H04L47/125 ,  H04L9/40 ,  H04L41/22 ,  H04L67/01 ,  G06F18/214

CPC classification number: H04L67/1012 ,  H04L47/781 ,  H04L47/125 ,  H04L63/20 ,  H04L41/22 ,  H04L67/01 ,  G06F18/2148

Abstract: The disclosure provides an approach for workload labeling and identification of known or custom applications. Embodiments include determining a plurality of sets of features comprising a respective set of features for each respective workload of a first subset of a plurality of workloads. Embodiments include identifying a group of workloads based on similarities among the plurality of sets of features. Embodiments include receiving label data from a user comprising a label for the group of workloads. Embodiments include associating the label with each workload of the group of workloads to produce a training data set. Embodiments include using the training data set to train a model to output labels for input workloads. Embodiments include determining a label for a given workload of the plurality of workloads by inputting features of the given workload to the model.

公开(公告)号：US20210336899A1

公开(公告)日：2021-10-28

申请号：US16855305

申请日：2020-04-22

Applicant: VMware, Inc.

Inventor： Alok TIAGI ,  Farzad GHANNADIAN ,  Karen HAYRAPETYAN ,  Laxmikant Vithal GUNDA ,  Sunitha KRISHNA ,  Ashot ASLANYAN ,  Anirban SENGUPTA

IPC: H04L12/911 ,  H04L12/803 ,  H04L12/24 ,  H04L29/06 ,  G06K9/62

Abstract: The disclosure provides an approach for workload labeling and identification of known or custom applications. Embodiments include determining a plurality of sets of features comprising a respective set of features for each respective workload of a first subset of a plurality of workloads. Embodiments include identifying a group of workloads based on similarities among the plurality of sets of features. Embodiments include receiving label data from a user comprising a label for the group of workloads. Embodiments include associating the label with each workload of the group of workloads to produce a training data set. Embodiments include using the training data set to train a model to output labels for input workloads. Embodiments include determining a label for a given workload of the plurality of workloads by inputting features of the given workload to the model.