One-shot models for local core network placement and traffic optimization in self deployable networks

Abstract

In recent years, we have witnessed a proliferation of core network (CN) virtualization, coupled with the emergence of self deployable architectures in cellular networks. These advances provide greater flexibility and allow CN functions to be virtualized and co-located with one or more base stations. However, they introduce new challenges, such as the optimal location of CN functions based on the physical architecture. In this paper, we tackle this problem and propose a new generic and efficient solution that locates the best base station to host the CN functions, while optimizing a given performance metric (e.g., maximum flow, max–min fairness, etc.) The core of our solution relies on jointly modeling the problem as a Mixed-Integer Nonlinear Programming (MINLP) and efficiently linearizing the nonlinear constraints. After presenting our model, we evaluate its performances, considering the global maximum flow as an optimization metric. The results showed that our joint modeling significantly reduces the run time compared to existing solutions while achieving the same optimal results. Furthermore, we highlight the genericity of our model, and we show how it can be easily adapted to handle different optimization metrics. We particularly consider some variants to address the fairness problem in conjunction with the local CN placement issue. We perform extensive simulations and discuss the obtained results. The latter allow us to derive insights regarding the tradeoff between maximum flow and fairness of proportional satisfaction, while highlighting the strengths of the proposed joint modeling.