Orchestration Strategies for Slicing in 5G Networks : Design and Performance Evaluation

Abstract: The advent of 5th generation of mobile networks (5G) will introduce new challenges for the infrastructure providers (InPs). One of the major challenges is to provide a common platform for supporting a large variety of services. Such a platform can be realized by creating slices, which can be dynamically scaled up/down according to variation of service requirements. An InP generates revenue by accepting a slice request, however it has to pay a penalty if a slice cannot be scaled up when required. Hence, an InP needs to design intelligent policies (e.g., using big data analytics (BDA) or reinforcement learning (RL)) which maximize its net profit.This thesis presents the design and performance evaluation of different orchestration strategies for dynamic slicing of infrastructure resources. Apart from simulation, some strategies are also experimentally demonstrated. The analysis is presented for both single-tenant and multi-tenant cases.For single-tenant case, this thesis proposes a dynamic slicing strategy for a centralized radio access network with optical transport. Results show that an InP needs to deploy 31.4% less transport resources when using dynamic slicing as compared to overprovisioning.  For multi-tenant case, this thesis presents MILP formulations and heuristic algorithms for dynamic slicing. Results show that, via dynamic slicing, it is possible to achieve 5 times lower slice rejection probability as compared to static slicing.The analysis is then extended to how BDA can be used in the slice admission and scaling processes. The proposed BDA-based admission policy increases the profit of an InP by up to 49% as compared to an admission policy without BDA. Moreover, the proposed BDA-based scaling policy lowers the penalty by more than 51% as compared to a first-come-first-served policy. Finally, this thesis presents how RL can be used for slice admission. The proposed policy performs up to 54.5% better as compared to deterministic heuristics.