Optimizing Network Slice Dimensioning via Resource Pricing

Network slicing has been viewed as a key enabler for the next-generation software-defined and cloud-based network (e.g., 5G and beyond) to accommodate diverse services in a flexible and cost-efficient fashion. Network slicing allows a network slice provider (NSP) to operate on a common network infrastructure to create customized isolated logical networks (i.e., network slices) for network slice customers (NSCs), (i.e., service providers). NSP and NSCs are independent operators who pursue profit maximization, while in the literature, only network cost optimization is intensively investigated in terms of service function chain embedding, i.e., virtual network function (VNF) placement and flow routing. Therefore, slices should be dimensioned (i.e., resources allocated to slices) according to the resource availability and the economic mechanism in the network, so as to optimize the resource utilization and improve the profit of NSP/NSCs. In this paper, we study elastic slice dimensioning with resource pricing as a Stackelberg pricing game, in which the NSP sells slices by pricing resources and NSCs adjust their slice's resource demand on VNF capacity and bandwidth, while both are trying to maximize their profit. Then, we formulate optimization problems for the pricing game and find that a closed form solution of the optimal price cannot be obtained for a non-trivial network. Hence, we propose a resource pricing algorithm that aims to maximize the NSP's profit and the network's social welfare. Compared with existing usage-based pricing method and two heuristic methods, our proposed pricing algorithm for slice dimensioning strikes a trade-off between maximizing NSP's profit and other metrics, including the resource utilization. Hence, it will helpfully exploiting the benefits of network slicing.