Data-Driven Adaptive Network Resource Slicing for Multi-Tenant Networks

Network slicing to support multi-tenancy plays a key role in improving the performance of 5G networks. In this paper, we propose a novel framework for network slicing with the goal of maximizing the expected utilities of tenants in the backhaul and Radio Access Network (RAN), where we reconfigure slices according to the time-varying user traffic and channel states. Upon the arrival of new statistics from users and channels and considering the expected utility from serving users of a slice and the reconfiguration cost, we formulate a sparse optimization problem to reconfigure resources for network slices with the maximum isolation of reserved resources. The formulated optimization is non-convex and difficult to solve. We use the group LASSO regularization and successive upper-bound minimization techniques to solve this problem by iteratively solving a sequence of convex approximations of the original problem. Simulation results verify that our approach outperforms the existing state of the art method.