  Multi-access Edge Computing (MEC) facilitates the deployment of critical
applications with stringent QoS requirements, latency in particular. This paper
considers the problem of jointly planning the availability of computational
resources at the edge, the slicing of mobile network and edge computation
resources, and the routing of heterogeneous traffic types to the various
slices. These aspects are intertwined and must be addressed together to provide
the desired QoS to all mobile users and traffic types still keeping costs under
control. We formulate our problem as a mixed-integer nonlinear program (MINLP)
and we define a heuristic, named Neighbor Exploration and Sequential Fixing
(NESF), to facilitate the solution of the problem. The approach allows network
operators to fine tune the network operation cost and the total latency
experienced by users. We evaluate the performance of the proposed model and
heuristic against two natural greedy approaches. We show the impact of the
variation of all the considered parameters (viz., different types of traffic,
tolerable latency, network topology and bandwidth, computation and link
capacity) on the defined model. Numerical results demonstrate that NESF is very
effective, achieving near-optimal planning and resource allocation solutions in
a very short computing time even for large-scale network scenarios.
