Edge computing shifts data processing from centralized servers to the network edge, where data is generated and consumed. This shift is critical to support applications in mobile and Internet of Things (IoT) environments, but it introduces two big challenges: edge nodes have limited computational resources, and network latency between nodes can be significant. Applications in this context are often structured as collections of micro-services, each of which must be strategically placed on edge nodes to minimize network latency, balance workload, and meet Service Level Agreements (SLAs). Additionally, minimizing energy consumption is crucial, which requires limiting the number of active edge nodes. As a result, determining optimal micro-service placement within an edge infrastructure is a complex problem, typically addressed using heuristics capable of producing effective solutions under diverse conditions. To address the inherently conflicting objectives of minimizing end-to-end latency, balancing workload across heterogeneous edge nodes, and minimizing energy consumption in micro-service placement, a multi‐objective optimization effectively exploring the trade-off between power consumption and performances in micro-service edge placement is required. Through a properly modified genetic algorithm that leverages Pareto‐front optimization and selection of active nodes number, in this paper we propose and evaluate a solution that dynamically identifies an appropriate subset of edge nodes and assigns micro-services, consistently delivering high‐quality placement strategies within a limited number of generations, and providing stable performance across a wide range of problem characteristics with minimal parameter tuning.
Mescoli et al. (Sun,) studied this question.