Machine learning-enabled queue management for network devices

A network device comprising:

• one or more network ports;
• one or more network interfaces coupled to respective ones of the one or more network ports;
• a media access control layer configured to communicate packets through the one or more network interfaces;
• multiple queues configured to buffer packets communicated through the one or more network interfaces;
• a switch controller configured to route the packets between the multiple queues;
• and
• a machine leaning-enabled (ML-enabled) queue manager associated with the multiple queues and a machine learning, ML, algorithm and configured to:
• initialize, with a randomized value and for a queue of the multiple queues, a queue management threshold configured to trigger a queue management policy of the queue;
• apply the queue management threshold to the queue of the network device;
• operate the network device to process packets through the queue based on the queue management threshold, the queue management threshold triggering the queue management policy for the queue through which the packets are processed;
• measure a performance metric of the queue associated with the packets processed through the queue based on the queue management threshold;
• provide, to a machine learning algorithm, an indication of the queue management threshold and an indication of the performance metric of the queue;
• receive, from the machine learning algorithm, an updated queue management threshold based on the queue management threshold and the performance metric of the queue;
• and
• apply the updated queue management threshold to the queue of the network device to configure the queue to process subsequent packets based on the updated queue management threshold being configured to trigger the queue management policy of the queue.

A system-on-chip (SoC) comprising:

• a media access control layer configured to communicate packets through one or more network interfaces;
• multiple queues configured to buffer packets communicated through the one or more network interfaces;
• a switch controller configured to route the packets between the multiple queues;
• and
• a machine learning-enabled (ML-enabled) queue manager associated with the multiple queues and a machine learning, ML, algorithm and configured to:
• initialize, with a randomized value and for a queue of the multiple queues, a queue management threshold of a queue management policy for the queue, the randomized value reduced over time;
• apply the queue management threshold to the queue of the SoC;
• operate the SoC to process packets through the queue based on the queue management threshold;
• measure a performance metric of the queue associated with the packets processed through the queue based on the queue management threshold;
• provide, to a machine learning algorithm, an indication of the queue management threshold and an indication of the performance metric of the queue;
• receive, from the machine learning algorithm, an updated queue management threshold based on the queue management threshold and the performance metric of the queue;
• and
• apply the updated queue management threshold to the queue of the SoC to configure the queue to process subsequent packets based on the updated queue management threshold of the queue management policy of the queue.