  The need to satisfy the QoS requirements of multiple network slices deployed
at the same base station poses a major challenge to network operators. The
problem becomes even harder when the desired QoS involves packet delays. In
that case, network utility maximization is not directly applicable since the
utilities of the slices are unknown. As a result, most related works learn
online the utilities of all slices and how to split the resources among them.
Unfortunately, this approach does not scale well for many slices. Instead, it
is needed to perform learning separately for each slice. To this end, we
develop a bandwidth demand estimator; a network function that periodically
receives as input the traffic of the slice and outputs the amount of bandwidth
that its MAC scheduler needs to deliver the desired QoS. We develop the
bandwidth demand estimator for QoS involving packet delay metrics based on a
model-based reinforcement learning algorithm. We implement the algorithm on a
cellular testbed and conduct experiments with time-varying traffic loads.
Results show that the algorithm delivers the desired QoS but with significantly
less bandwidth than non-adaptive approaches and other baseline online learning
algorithms.
