Resource Allocation for Network Slicing in 5G Telecommunication Networks: A Survey of Principles and Models

TLDR

5G networks must support rapidly growing, diverse demands, and network slicing—enabled by SDN and NFV—offers customizable end‑to‑end logical networks that tailor resources to meet varying QoS and SLA requirements while improving load balancing and performance. This survey examines the principles and models underlying resource‑allocation algorithms for 5G network slicing. The authors review SDN/NFV fundamentals, the MO architecture, resource types and isolation levels in RAN and CN slicing, categorize mathematical allocation models by objective, and highlight open research challenges with proposed solutions.

Abstract

With the rapid and sustained growth of network demands, 5G telecommunication networks are expected to provide flexible, scalable, and resilient communication and network services, not only for traditional network operators, but also for vertical industries, OTT, and third parties to satisfy their different requirements. Network slicing is a promising technology to establish customized end-to-end logic networks comprising dedicated and shared resources. By leveraging SDN and NFV, network slices associated with resources can be tailored to satisfy diverse QoS and SLA. Resource allocation of network slicing plays a pivotal role in load balancing, resource utilization, and networking performance. In this article, we focus on the principles and models of resource allocation algorithms in 5G network slicing. We first introduce the basic ideas of the SDN and NFV with their roles in network slicing. The MO architecture of network slicing is also studied, which provides a fundamental framework of resource allocation algorithms. Then, resource types with corresponding isolation levels in RAN slicing and CN slicing are analyzed, respectively. Furthermore, we categorize the mathematical models of resource allocation algorithms based on their objectives and elaborate them with typical examples. Finally, open research issues are identified with potential solutions.