Admission control and pricing for multi-tenant network slices in 5G: A learning perspective

Network slicing is a critical component in 5G, where each slice can be customized for a given type of service, and a given tenant characterized by a stochastic demand and a resource utility function reflecting its Quality of Service requirements. Considering the slice market as a Stackelberg game, the operator, as the leader, presents pricing for each demand interval and slice type, and the tenants, as followers, decide which demand interval to request. The operator jointly determines the pricing and admission that maximize its revenue while satisfying its capacity constraint. We show NP-hardness of this problem, and a paradox that the operator's revenue could decrease if a tenant's resource utility increases. We consider a practical scenario where the tenants’ resource utilities are not known to the operator. For learning an optimal pricing, we propose online approaches based on iteratively updating the operator's knowledge regarding the resource utilities post its interactions with the tenants, and an offline approach based on neural networks. We study these with respect to various metrics: achieved revenue, reliability, and learning rate. One of our approaches consistently achieves near-optimal revenue irrespective of the number of tenants, with over 95% of optimal revenue within 20 interactions on average.