公开(公告)号：US20210173688A1

公开(公告)日：2021-06-10

申请号：US16787050

申请日：2020-02-11

Applicant: VMWARE, INC.

Inventor： MADAN SINGHAL ,  Gyan Sinha ,  Abhijit Sharma ,  Ashutosh Kulkarni ,  Avinash Nigam ,  Shivam Pawar

IPC: G06F9/455 ,  H04L29/08 ,  G06F17/16 ,  G06N20/00

Abstract: A feature selection methodology is disclosed. In a computer-implemented method, components of a computing environment are automatically monitored, and have a feature selection analysis performed thereon. Provided the feature selection analysis determines that features of the components are well defined, a clustering of the features is performed. Provided the feature selection analysis determines that features of the components are well defined, a similarity analysis of the sub-features of the feature is performed. Results of the feature selection methodology are generated.

公开(公告)号：US20220376970A1

公开(公告)日：2022-11-24

申请号：US17325077

申请日：2021-05-19

Applicant: VMware, Inc.

Inventor： Rahul Chawathe ,  Gyan Sinha ,  Amarjit Gupta

IPC: H04L12/24 ,  H04L12/26

Abstract: Computational methods and systems troubleshoot problems in a data center network. A dependency graph is constructed in response to an entity of the network exhibiting anomalous behavior. The dependency graph comprises nodes that correspond to metrics of entities that transmit data to and receive data from the entity over the network and edges that represent a connection between metrics. An anomaly score is determined for each metric of the dependency graph. Correlated metrics connected by the edges of the dependency graph are determined. Time-change events of the metrics of the dependency graph are also identified. Each metric of the dependency graph is rank ordered based on the anomaly scores, correlations with other metrics, and the time-change events. Higher ranked metrics are more likely associated with a problem in the network that corresponds to the anomalous behavior of the entity.