Dynamic rerouting of wireless traffic based on input from machine learning-based mobility path analysis

An apparatus comprising:

• one or more network interfaces to communicate with a network;
• a processor coupled to the network interfaces and configured to execute one or more processes;
• and
• a memory configured to store a process executable by the processor, the process when executed configured to:
• receive data indicative of roaming failures along mobility paths in the network, wherein the mobility paths represent ordered series of wireless access points via which wireless clients have accessed the network over time;
• use, based on the data indicative of the roaming failures, a machine learning-based model to associate mobility path failure metrics with portions of the mobility paths;
• receive a threshold mobility path failure metric from the particular client device;
• identify, for a first mobility path, an alternate mobility path that has a lower mobility path failure metric than that of the first mobility path based on an output from the machine learning model;
• and
• trigger a mobility path reroute for a particular client device in the network on the first mobility path to the alternate mobility path based in part on a determination that the mobility path metric associated with the alternate mobility path is lower than the received threshold.

A tangible, non-transitory, computer-readable medium storing program instructions that cause a device to execute a process comprising:

• receiving data indicative of roaming failures along mobility paths in a network, wherein the mobility paths represent ordered series of wireless access points via which wireless clients have accessed the network over time;
• using, based on the data indicative of the roaming failures, a machine learning-based model to associate mobility path failure metrics with portions of the mobility paths;
• receiving a threshold mobility path failure metric from the particular client device;
• identifying, for a first mobility path, an alternate mobility path that has a lower mobility path failure metric than that of the first mobility path based on an output from the machine learning model;
• and
• triggering a mobility path reroute for a particular client device in the network on the first mobility path to the alternate mobility path based in part on a determination that the mobility path metric associated with the alternate mobility path is lower than the received threshold.