  Interactive applications with automated feedback will largely influence the
design of future networked infrastructures. In such applications, status
information about an environment of interest is captured and forwarded to a
compute node, which analyzes the information and generates a feedback message.
Timely processing and forwarding must ensure the feedback information to be
still applicable; thus, the quality-of-service parameter for such applications
is the end-to-end latency over the entire loop. By modelling the communication
of a feedback loop as a two-hop network, we address the problem of allocating
network resources in order to minimize the delay violation probability (DVP),
i.e. the probability of the end-to-end latency exceeding a target value. We
investigate the influence of the network queue states along the network path on
the performance of semi-static and dynamic scheduling policies. The former
determine the schedule prior to the transmission of the packet, while the
latter benefit from feedback on the queue states as time evolves and reallocate
time slots depending on the queue's evolution. The performance of the proposed
policies is evaluated for variations in several system parameters and
comparison baselines. Results show that the proposed semi-static policy
achieves close-to-optimal DVP and the dynamic policy outperforms the
state-of-the-art algorithms.
