  The increasing demand for latency-sensitive applications has necessitated the
development of sophisticated algorithms that efficiently manage packets with
end-to-end delay targets traversing the networked infrastructure. Network
components must consider minimizing the packets' end-to-end delay violation
probabilities (DVP) as a guiding principle throughout the transmission path to
ensure timely deliveries. Active queue management (AQM) schemes are commonly
used to mitigate congestion by dropping packets and controlling queuing delay.
Today's established AQM schemes are threshold-driven, identifying congestion
and trigger packet dropping using a predefined criteria which is unaware of
packets' DVPs. In this work, we propose a novel framework, Delta, that combines
end-to-end delay characterization with AQM for minimizing DVP. In a queuing
theoretic environment, we show that such a policy is feasible by utilizing a
data-driven approach to predict the queued packets' DVPs. That enables Delta
AQM to effectively handle links with arbitrary stationary service time
processes. The implementation is described in detail, and its performance is
evaluated and compared with state of the art AQM algorithms. Our results show
the Delta outperforms current AQM schemes substantially, in particular in
scenarios where high reliability, i.e. high quantiles of the tail latency
distribution, are of interest.
