  Network slicing enables the deployment of multiple dedicated virtual
sub-networks, i.e. slices on a shared physical infrastructure. Unlike
traditional one-size-fits-all resource provisioning schemes, each network slice
(NS) in 5G is tailored to the specific service requirements of a group of
customers. An end-to-end (E2E) mobile NS orchestration requires the
simultaneous provisioning of computing, storage, and networking resources
across the core network (CN) and the radio access network (RAN). Constant
temporospatial changes in mobile user demand profiles further complicate the
E2E NSs resource provisioning beyond the limits of the existing best-effort
schemes that are only effective under accurate demand forecasts for all slices.
This paper proposes a practical two-time-scale resource provisioning framework
for E2E network slicing under demand uncertainty. At each macro-scale instance,
we assume that only the spatial probability distribution of the NS demands is
available. We formulate the NSs resource allocation problem as a stochastic
mixed integer program (SMIP) with the objective of minimizing the total
resource cost at the CN and the RAN. At each microscale instance, utilizing the
exact slice demand profiles, a linear program is solved to jointly minimize the
unsupported traffic and the resource cost at the RAN. We verify the
effectiveness of our resource allocation scheme through numerical experiments.
