Abstract: A computer-implemented method, according to one approach, includes: receiving a user dataset having textual entries, and sampling a number of the textual entries in the received user dataset. The textual entries sampled from the user dataset are applied to an encoding model that corresponds to a given large language model architecture. The vectors that are produced by the encoding model are combined. The combined vectors are further sent to a vector database. In response, one or more public datasets are received from the vector database, the one or more public datasets being a closest match to the combined vectors.

Abstract: An embodiment establishes a virtual representation of a network configuration, wherein the virtual representation of the network configuration comprises a plurality of layers corresponding to the network configuration. The embodiment generates a plurality of workloads to simulate with the virtual representation. The embodiment generates a plurality of workload statuses to simulate with the virtual representation. The embodiment simulates the plurality of workloads. The embodiment simulates the plurality of workloads statuses. The embodiment receives simulation results responsive to simulating the plurality of workloads and from simulating the plurality of workload statuses.

Abstract: An embodiment establishes a virtual representation of a network configuration, wherein the virtual representation of the network configuration comprises a plurality of layers corresponding to the network configuration. The embodiment generates a plurality of workloads to simulate with the virtual representation. The embodiment generates a plurality of workload statuses to simulate with the virtual representation. The embodiment simulates the plurality of workloads. The embodiment simulates the plurality of workloads statuses. The embodiment receives simulation results responsive to simulating the plurality of workloads and from simulating the plurality of workload statuses.

Abstract: A computer-implemented method, according to one approach, includes: receiving a user dataset having textual entries, and sampling a number of the textual entries in the received user dataset. The textual entries sampled from the user dataset are applied to an encoding model that corresponds to a given large language model architecture. The vectors that are produced by the encoding model are combined. The combined vectors are further sent to a vector database. In response, one or more public datasets are received from the vector database, the one or more public datasets being a closest match to the combined vectors.

Abstract: A computer-implemented method, according to one approach, includes obtaining, on a first edge device, a plurality of artificial intelligence (AI) rules. The method further includes applying, on the first edge device, the AI rules to a plurality of evaluated data samples for determining whether to include the data samples in an inspection collected data set. In response to a determination that a first of the data samples satisfies each of the AI rules, the first data sample is caused to be included in the inspection collected data set. The inspection collected data set is caused to be uploaded to a cloud site. A computer program product, according to another approach, includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a first edge device to cause the first edge device to perform any combination of features of the foregoing methodology.

Abstract: An example system includes a processor to receive state bundles including full state bundles and delta bundles with dependencies corresponding to updates received via a transport layer from a lower layer in a multi-layer edge architecture. The processor can conflate the state bundles to generate ready to be processed bundles. The processor can then process the ready to be processed bundles.