  Network slicing to support multi-tenancy plays a key role in improving the
performance of 5G networks. In this paper, we propose a two time-scale
framework for the reservation-based network slicing in the backhaul and Radio
Access Network (RAN). In the proposed two time-scale scheme, a subset of
network slices is activated via a novel sparse optimization framework in the
long time-scale with the goal of maximizing the expected utilities of tenants
while in the short time-scale the activated slices are reconfigured according
to the time-varying user traffic and channel states. Specifically, using the
statistics from users and channels and also considering the expected utility
from serving users of a slice and the reconfiguration cost, we formulate a
sparse optimization problem to update the configuration of a slice resources
such that the maximum isolation of reserved resources is enforced. The
formulated optimization problems for long and short time-scales are non-convex
and difficult to solve. We use the $\ell_q$-norm, $0<q<1$, and group LASSO
regularizations to iteratively find convex approximations of the optimization
problems. We propose a Frank-Wolfe algorithm to iteratively solve approximated
problems in long time-scales. To cope with the dynamical nature of traffic
variations, we propose a fast, distributed algorithm to solve the approximated
optimization problems in short time-scales. Simulation results demonstrate the
performance of our approaches relative to optimal solutions and the existing
state of the art method.
