  Advances in network programmability enable operators to 'slice' the physical
infrastructure into independent logical networks. By this approach, each
network slice aims to accommodate the demands of increasingly diverse services.
However, precise allocation of resources to slices across future 5G
millimetre-wave backhaul networks, to optimise the total network utility, is
challenging. This is because the performance of different services often
depends on conflicting requirements, including bandwidth, sensitivity to delay,
or the monetary value of the traffic incurred. In this paper, we put forward a
general rate utility framework for slicing mm-wave backhaul links, encompassing
all known types of service utilities, i.e. logarithmic, sigmoid, polynomial,
and linear. We then introduce DELMU, a deep learning solution that tackles the
complexity of optimising non-convex objective functions built upon arbitrary
combinations of such utilities. Specifically, by employing a stack of
convolutional blocks, DELMU can learn correlations between traffic demands and
achievable optimal rate assignments. We further regulate the inferences made by
the neural network through a simple 'sanity check' routine, which guarantees
both flow rate admissibility within the network's capacity region and minimum
service levels. The proposed method can be trained within minutes, following
which it computes rate allocations that match those obtained with
state-of-the-art global optimisation algorithms, yet orders of magnitude
faster. This confirms the applicability of DELMU to highly dynamic traffic
regimes and we demonstrate up to 62% network utility gains over a baseline
greedy approach.
