Adaptive multicast data rate configuration in managed Wi-Fi networks to improve throughput using unsupervised machine learning

A non-transitory computer-readable medium storing instructions that, when executed by a processor, perform a computer-implemented method for using machine learning to dynamically adjust multicast data rates for service set identifier (SSID) connections based on real-time network statistics for an enterprise network, the method comprising:

• monitoring an SSID, with an exchange of data packets over the enterprise network between network devices, to collect real-time network traffic statistics associated with the SSID as a whole and each station utilizing the SSID;
• deriving, with a processor of the network device, a baseline throughput and a baseline multicast traffic for the SSID from the network traffic statistics using unsupervised machine learning that utilizes K-means clustering to cluster the real-time network traffic statistics;
• detecting a deterioration in the real-time network traffic statistics for the SSID in relation to the baseline throughput and the baseline multicast traffic;
• responsive to the deterioration detection, determining if a lowest unicast data rate is higher than a current multicast data rate for the SSID;
• and
• responsive to the lowest unicast data rate being higher than the current multicast data rate for the SSID, adjusting the multicast data rate to match the lowest unicast data rate for the SSID.

A network device to use machine learning to dynamically adjust multicast data rates for service set identifier (SSID) connections based on real-time network statistics for an enterprise network, the network device comprising:

• a processor;
• a network interface communicatively coupled to the processor and to the hybrid wireless network;
• and
• a memory, communicatively coupled to the processor and storing:
• an SSID monitoring module to track an SSID, with an exchange of data packets over the enterprise network between network devices, to collect real-time network traffic statistics associated with the SSID as a whole and each station utilizing the SSID;
• a baseline throughput module to derive and a baseline multicast traffic for the SSID from the network traffic statistics using unsupervised machine learning that utilizes K-means clustering to cluster the real-time network traffic statistics;
• a deterioration module to detect in the real-time network traffic statistics for the SSID in relation to the baseline throughput and the baseline multicast traffic;
• a multicast rate adjustment module to, responsive to the deterioration detection, determine if a lowest unicast data rate is higher than a current multicast data rate for the SSID, and responsive to the lowest unicast data rate being higher than the current multicast data rate for the SSID, adjust the multicast data rate to match the lowest unicast data rate for the SSIDs.