PROJECT TITLE :

Latency and Mobility–Aware Service Function Chain Placement in 5G Networks

ABSTRACT:

It is anticipated that 5G networks will be able to support a wide variety of cutting-edge services and applications that have diverse quality of service (QoS) requirements. These requirements may include high data transfer rates and low end-to-end (E2E) latency. It is generally accepted that end-to-end (E2E) latency can be decreased by relocating computational capability to locations closer to the edge of the network. However, the limited amount of computational resources of the edge nodes presents a challenge in terms of utilizing these resources in an efficient manner while simultaneously satisfying QoS requirements. In this study, we use mixed-integer linear programming (MILP) techniques to formulate and solve a joint user association, service function chain (SFC) placement, where SFCs are composed of virtualized service functions (VSFs), and resource allocation problem in 5G networks that are composed of decentralized units (DUs), centralized units (CUs), and a core network. SFCs are composed of virtualized service functions (VSFs), and resource allocation problem in 5G networks (5GC). In particular, we analyze and contrast four different strategies for resolving the issue. The E2E latency that users experience can be reduced with the first two approaches, while the cost of providing the service can also be reduced with these approaches. The other two strategies instead focus on reducing the number of VSF migrations and the negative effects those migrations have on the overall quality of the user experience. The third strategy also works to reduce the number of inter-CU handovers. The scalability problem of the MILP-based solutions is then approached with the help of a heuristic that we propose. The effectiveness of the heuristic algorithm that was proposed was demonstrated by the results of simulations.