At&t Intellectual Property I, L.p.

Harvard College

University of Southern California

Sivaramakrishnan Ramanathan

Balachander Krishnamurthy

Yaron Kanza

Minian Yu

Jelena Mirkovic

A processing system including at least one processor may obtain a time series of measurement values from a communication network and train a prediction model in accordance with the time series of measurement values to predict future instances of an event of interest, where the time series of measurement values is labeled with one or more indicators of instances of the event of interest. The processing system may then generate a deterministic finite automaton based upon the prediction model, convert the deterministic finite automaton into a rule set, and deploy the rule set to at least one network component of the communication network.

Prediction of network events via rule set representations of machine learning models

In one embodiment, a method includes determining, by one or more processors, a weight of a link between a first node and a second node of a network, wherein the weight is proportional to a probability value of forwarding a probe packet from the first node to the second node of the network. The method also includes adjusting, by the processors, the weight of the link between the first node and the second node using binary exponential backoff. The method further includes determining, by the processors, to forward the probe packet to the second node of the network based on the adjusted weight of the link and one or more field values of the probe packet.

Software defined prober

Growing community of inventors

Los Angeles, CA, United States of America

Sivaramakrishnan Ramanathan