Smart Cities and Industry 4.0 have opened new application scenarios posing new challenges to networking services. The continuously growing demand for high quality of service (QoS) and the intrinsic geographical distribution of this novel class of applications require novel and tailored deployment solutions. Microservice applications placement in edge computing requires balancing resource constraints, network efficiency, and end-to-end latency constraints. This work presents Centrality-based Resource-Orchestrator System (CeROs), a novel Mixed Integer Linear Programming (MILP) model that introduces an objective function based on the structural node importance based on its position within the network’s topology, referred as node centrality, which promotes efficient resource usage across the network without directly optimizing latency or individual resource consumption. This approach ensures a generalizable and unbiased formulation that can be adjusted to various deployment scenarios. The experiments have been conducted over synthetic yet realistic cloud-to-edge network topologies and microservice applications, modeled to reflect real-world resource constraints, communication delays, and deployment challenges. The evaluations demonstrate that CeROs outperforms trivial placement strategies, achieving competitive latency even in strict scenarios and more balanced resource usage, resulting in a lower node costs. CeROs does not force optimization toward a single metric, making it more robust across different emerging environments as like smart cities with the Autonomous Vehicle Driving application.