Optimizing Network Slice Dimensioning via Resource Pricing

Abstract

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 costefficient 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.

Slicing Resource Allocation for eMBB and URLLC in 5G RAN

“…Regarding the single service of RAN slicing, the researchers focused on the price of slice resources [3,8,[14][15][16] and the throughput [17,18]. In [3], wang et al studied resource pricing as a Stackelberg pricing game, in which network slice customers (NSCs) maximize their profit by adjusting their slice's resource requirement. Lee et al [18] considered the multitenant cellular network slicing, aiming to achieve a higher network throughput, fairness, and QoS performance.…”

“…methods to meet the different requirements of services with multiple logical networks. It has been a key enabler for 5G to accommodate a variety of services in a flexible manner [3]. erefore, it is critical to study multiservice problems in the virtual radio access network (RAN) by network slicing, especially different service requirements in various scenarios, such as enhanced mobile broadband (eMBB), ultrareliable low latency communication (URLLC), and massive machine-type communication (mMTC) [4,5].…”

OPEX-Limited 5G RAN Slicing: an Over-Dataset Constrained Deep Learning Approach

“…In [5], the authors have studied elastic slice dimensioning with resource pricing as a Stackelberg pricing game, in which the network service provider (NSP) sells slices by pricing resources and network slice customers (NSCs) adjust their slice's resource demand on VNF capacity and bandwidth, while both are trying to maximize their profit.…”

A Survey on Slice Admission Control Strategies and Optimization Schemes in 5G Network

“…Unlike the 4G network, the 5G infrastructure comprises the following important features: network function virtualization (NFV), software-defined networking (SDN) and network slicing. Furthermore, network slicing is a principal enabler of network resource management and allocation [1]. It allows multiple logical networks to be deployed on a common physical infrastructure in order to provide the quality of service (QoS) required for divergent applications [2], [3].…”