Scalable Multi-objective Optimization of Reliable Latency-constrained Optical Transport Networks

In the evolving scenario of 5G end-to-end networks, optical transport networks provide the connectivity between the mobile edge and the mobile core network. According to the functional decoupling of the base station into the Remote Radio Unit (RRU) and the Baseband Unit (BBU), the latter can be virtualized into a cloud computing platform to access the mobile core. As a consequence, BBU virtual network functions related to different RRUs can be centralized and replicated in a subset of the nodes of the transport network with the aim of optimized reliable design. In this paper a scalable methodology, based on lexicographic optimization, is proposed for the solution of a multi-objective optimization problem to achieve, among other goals, the minimization of the number of active nodes in the transport network while supporting reliability and meeting latency constraints. The proposed solution method is compared to an aggregate optimization approach, showing that the former is capable of proving the optimality of the most relevant components of the multi-objective function (minimization of the number of active nodes and of the number of hops) for instances of medium size, and finds better solutions for instances with a larger number of nodes, namely several tens. The computing times to find an optimal solution for the most relevant objectives are much shorter than those required to solve the aggregate model, even for networks of several tens of nodes.