Slice-aware 5G network orchestration framework based on dual-slice isolation and management strategy (D-SIMS).

ABSTRACT

Network slicing is crucial to the 5G architecture because it enables the virtualization of network resources into a logical network. Network slices are created, isolated, and managed using software-defined networking (SDN) and network function virtualization (NFV). The virtual network function (VNF) manager must devise strategies for all stages of network slicing to ensure optimal allocation of physical infrastructure (PI) resources to high-acceptance virtual service requests (VSRs). This paper investigates two independent network slicing frameworks named as dual-slice isolation and management strategy (D-SIMS) and recommends the best of the two based on performance measurements. D-SIMS places VNFs for network slicing using self-sustained resource reservation (SSRR) and master-sliced resource reservation (MSRR), with some flexibility for the VNF manager to choose between them based on the degree to which the underlying physical infrastructure has been sliced. The present research work consists of two phases the first deals with the creation of slices, and the second with determining the most efficient way to distribute resources among them. A deep neural network (DNN) technique is used in the first stage to generate slices for both PI and VSR. Then, in the second stage, we propose D-SIMS for resource allocation, which uses both the fuzzy-PROMETHEE method for node mapping and Dijkstra's algorithm for link mapping. During the slice creation phase, the proposed DNN training method's classification performance is evaluated using accuracy, precision, recall, and F1 score measures. To assess the success of resource allocation, metrics such as acceptance rate and resource effectiveness are used. The performance benefit is investigated under various network conditions and VSRs. Finally, to demonstrate the importance of the proposed work, we compare the simulation results to those in the academic literature.