In 5G networks, network slicing is an essential technology that enables effective resource allocation for a wide variety of services and applications. In the review, the several approaches to network slicing, such as static, dynamic, SDN-based, NFV-based, and hybrid, as well as the benefits and drawbacks of each approach, are broken down and analysed. This analysis also highlights the use of sophisticated techniques such as reinforcement learning and machine learning to optimise the decisions made on network slicing and increase network performance. In addition, the paper discusses the safety issues that are related with network slicing and underlines the need for more research to develop new methods and frameworks that would enable network slicing in 5G networks to be carried out in an efficient and effective manner. The most important conclusions from this analysis can help direct future research on network slicing, and its techniques can assist network operators in selecting the solution that is the most appropriate for the requirements of their particular use case and applications.

5G, network slicing, resource allocation, virtual networks, SDN, NFV, machine learning, reinforcement learning, security, privacy, network complexity