US 12,457,173 B2
Autonomous traffic (self-driving) network with traffic classes and passive and active learning
Mariam Kiran, San Ramon, CA (US); Nicholas Buraglio, Berkeley, CA (US); and Scott Campbell, Lake Oswego, OR (US)
Assigned to THE REGENTS OF THE UNIVERSITY OF CALIFORNIA, Oakland, CA (US)
Filed by THE REGENTS OF THE UNIVERSITY OF CALIFORNIA, Oakland, CA (US)
Filed on Nov. 4, 2022, as Appl. No. 18/052,614.
Claims priority of provisional application 63/276,148, filed on Nov. 5, 2021.
Prior Publication US 2023/0145097 A1, May 11, 2023
Int. Cl. H04L 47/127 (2022.01); H04L 41/147 (2022.01); H04L 41/16 (2022.01); H04L 47/2483 (2022.01)
CPC H04L 47/127 (2013.01) [H04L 41/147 (2013.01); H04L 41/16 (2013.01); H04L 47/2483 (2013.01)] 20 Claims
OG exemplary drawing
 
1. An apparatus for autonomous network traffic management, comprising:
(a) a non-transitory medium storing instructions executable by said one or more processors;
(b) wherein said instructions, when executed by at least one of the processors, perform steps comprising:
(i) detecting and extracting real-time network monitoring data as network traffic profiles from a network, and network health data from the network wherein said network health data comprises a historical database of recent network monitoring data that has been analyzed as endpoint behaviors;
(ii) predicting future network statistics based on the network traffic profiles and the network health data;
(iii) creating traffic classes from patterns in the network traffic profiles and the network health data for use as input to an artificial intelligence instance;
(iv) simulating traffic over the network for each of the traffic classes;
(v) optimizing network path routing and prioritizing traffic based on network optimization objectives directed toward meeting user objectives, by utilizing deep reinforcement learning using the predicted future network statistics; and
(vi) implementing the optimized network path routing over the network for particular network traffic profiles.