Abstract: Systems and methods are described for providing a graphical user interface (“GUI”) for graph-based visualizations of network flows using entity clustering. In an example, an application service can periodically execute a job for assigning network entities to clusters according to a clustering type. The job can also include creating metadata about each cluster. The application service can store the assignments in a cache. A user can select to display a visualization of the network in the GUI based on the clustering type. The application service can detect any entities without cluster assignments and create a best guess assignment for them. The GUI can then display a visualization of the network according to the selected clustering type.

Abstract: The disclosure herein describes managing the execution of ML pipelines based at least in part on a dependency graph using a feature service. A plurality of feature creator processes are scheduled for execution using a set of feature creation resources. The scheduling is based at least in part on a dependency graph which describes dependency relationships between the plurality of feature creator processes and raw data sets stored in a raw data cache. The scheduled feature creator processes are then executed, wherein feature sets are created from the executed feature creator processes. The features sets are stored in a feature cache and the stored feature sets are exposed to a feature consumer using a feature interface. The use of the dependency graph and the raw data and feature caches enables the disclosure to reduce duplicated effort and resource usage across multiple pipelines that are executed on the system.

Abstract: An example method of analyzing workloads executing in a network managed virtualized computing system includes: presenting, by a network analyzer, a first view of the workloads on a canvas to a first user; receiving, at the network analyzer, first user input to create a first label, the first label initially disassociated with security policies of a network manager managing a software defined network used by the workloads; receiving, at the network analyzer, second user input to assign the first label to at least one of the workloads; and presenting, by the network analyzer, a second view of the workloads on the canvas to the first user having a first group of the at least one workload and a second group having workloads other than the at least one workload.

Abstract: Some embodiments provide a mechanism to automatically group workloads of a network into clusters of related workloads. The method of some embodiments displays consolidated workload data for a network. The method, for each of multiple workloads: (1) receives a set of identifiers characterizing the workload; and (2) converts the set of identifiers to a vector representation of the workload. The method then identifies clusters of workloads based on the vector representations of the workloads. The method then displays the workloads grouped in the identified clusters and displays data flows between the clusters of workloads. Converting the set of identifiers to a vector representation of the workload may include applying a similarity metric to the set of identifiers.