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Active Queue Management (AQM) is a mechanism employed to alleviate transientcongestion in network device buffers, such as routers and switches. TraditionalAQM algorithms use fixed thresholds, like target delay or queue occupancy, tocompute random packet drop probabilities. A very small target delay canincrease packet losses and reduce link utilization, while a large target delaymay increase queueing delays while lowering drop probability. Due to dynamicnetwork traffic characteristics, where traffic fluctuations can lead tosignificant queue variations, maintaining a fixed threshold AQM may not suitall applications. Consequently, we explore the question: \textit{What is theideal threshold (target delay) for AQMs?} In this work, we introduce DESiRED(Dynamic, Enhanced, and Smart iRED), a P4-based AQM that leverages precisenetwork feedback from In-band Network Telemetry (INT) to feed a DeepReinforcement Learning (DRL) model. This model dynamically adjusts the targetdelay based on rewards that maximize application Quality of Service (QoS). Weevaluate DESiRED in a realistic P4-based test environment running an MPEG-DASHservice. Our findings demonstrate up to a 90x reduction in video stall and a42x increase in high-resolution video playback quality when the target delay isadjusted dynamically by DESiRED.

DESiRED -- Dynamic, Enhanced, and Smart iRED: A P4-AQM with Deep Reinforcement Learning and In-band Network Telemetry