| CPC H04L 43/065 (2013.01) [H04L 41/16 (2013.01); H04L 43/0817 (2013.01); H04L 45/28 (2013.01)] | 20 Claims |
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1. A machine learning (ML) based system for network resilience and steering comprising:
a non-transitory storage device; and
a processor coupled to the non-transitory storage device, wherein the processor is to:
monitor data movement across one or more network ports;
extract network performance indicators associated with the data movement;
determine, via a machine learning (ML) subsystem, a likelihood of failure for an operational first network port based on at least the network performance indicators;
determine that a status of the first network port is indicative of operational failure in an instance in which the likelihood of failure for the first network port satisfies a failure threshold;
determine that the first network port is associated with a first network port cluster;
determine a redundant network port and an intermediate network switch associated with the first network port cluster;
trigger the intermediate network switch to reroute a portion of network traffic from the first network port to the redundant network port in response to the status of the first network port, by terminating a communication link to the first network port; and
re-trigger the intermediate switch to reroute the portion of network traffic back to the first network port upon detecting recovery of the first network port, by re-establishing the communication link.
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