Machine learning-enabled queue management for network devices

Abstract

The present disclosure describes apparatuses and methods for machine learning-enabled (ML-enabled) queue management for network devices. In some aspects, an ML-enabled queue manager of a network device initializes a queue management setting with a randomized value and the device processes packets through the queue based on the queue management setting. The ML-enabled queue manager measures a performance metric of the queue and provides, to an ML algorithm, an indication of the queue management setting and an indication of the performance metric of the queue. The ML-enabled queue manager then receives, from the machine learning algorithm, an updated queue management setting and configures the queue with the updated queue management setting to process subsequent packets based on the updated queue management setting. By so doing, the ML-enabled queue manager may tune one or more queue management settings of the queue to optimize performance of the network device.