Optimization of B5G Network Slicing for Smart Cities Applications Using the MGBFS Algorithm

This paper introduces the matroid-based modified greedy breadth-first search (MGBFS) algorithm, a pioneering approach tailored for optimizing network slicing for smart city applications. It adeptly allocates virtual network functions and virtual links, considering constraints while optimizing for cost and latency considerations. This approach extends beyond the boundaries of the access network, offering a comprehensive solution spanning the edge, transport, and core network domains. Additionally, we propose a dual integer linear programming (ILP) optimization problem to jointly minimize the embedding cost and latency of the network. An extensive comparison of the MGBFS algorithm with an existing baseline model and ILP confirms the superiority of our proposed method.