Multi-objective hierarchical edge infrastructure design for service chain workloads: A MOEA/D-driven joint planning and operation approach

Edge computing is poised to become a cornerstone of the emerging 6G landscape, where an ever-growing class of ultra-low-latency applications must be served close to the user. Despite its promise, real-world deployments remain nascent, with large-scale implementations anticipated by both Communication and Digital Service Providers (CSPs/DSPs) within the following years. Consequently, strategic edge network design is essential not only to maximize performance, but also to avoid redundant investments that can lead to an increased sum of Capital and Operational Expenditures (CAPEX/OPEX). In this work, we address a tri-fold problem: (i) the selection of deployment locations, (ii) the configuration of devices at the chosen location sites, and (iii) the assignment of the projected workload. Our objective is formulated as a weighted combination of the edge infrastructure’s establishment cost, the expected cumulative workload latency and the total expected energy consumption in the operational phase. To capture the spatial and temporal variability of demand, we solve the assignment subproblem over distinct snapshots, each representing a unique workload projection. We first present a Mixed Integer Linear Programming (MILP) formulation that yields the optimal solution; however, due to its computational intractability, we propose a novel adaptation of the Multi-Objective Evolutionary Algorithm by Decomposition (MOEA/D), with an embedded heuristic algorithm to assist in the chromosome fitness calculation. This method leverages the similarity among neighboring subproblems in a multi-objective framework to efficiently approximate the underlying Pareto frontier. In the experiments, the proposed method is contrasted with a sophisticated single-objective Rollout approach. Our results demonstrate the benefits of adopting a multi-objective algorithm in terms of performance, stability and interpretability across different scalarized subproblems. The proposed framework offers a practical intent-based decision support tool for edge infrastructure providers, weighing CAPEX against operating objectives ahead of initial deployment.