E-SPLIT: A Hierarchical Genetic Algorithm for Energy-Efficient Distributed AI Services

As we progress toward a new era of Artificial Intelligence (AI)-enabled wireless networks, the focus shifts to deploying distributed intelligence to enhance network automation, scalability, and responsiveness. Despite its merits, it often leads to resource-intensive deployments, which raise energy concerns. These concerns are further amplified by the limited availability of resource orchestration strategies capable of addressing the multi-faceted nature of distributed AI. This work targets energy consumption minimization of distributed AI services by proposing a custom meta-heuristic, two-tier hierarchical genetic algorithm (HGA) that integrates a divide-and-conquer strategy to provide effective chained decision-making. The first tier of HGA determines the optimal placement of model partitions within an AI service on the underlying network, while the second tier focuses on strategic resource allocation for each partition, ensuring that service latency requirements are met. A safe strategy selection is proposed, applying a custom repair mechanism and a penalty function that discourages constraints violation. Evaluation results show the effectiveness and robustness of the proposed HGA, compared to two state-of-the-art baseline solutions, on different network environments and evaluation scenarios. HGA achieves up to 94.1% decrease in the total energy consumption per service compared to the baselines, while entirely eliminating infeasible strategies.