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Network slicing to support multi-tenancy plays a key role in improving the performance of 5G and beyond networks. In this paper, we study dynamically slicing network resources in the backhaul and Radio Access Network (RAN) prior to user demand observations across multiple tenants, where each tenant owns and operates several slices to provide different services to users. 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
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