GENO: Diversity-Preserving Green Optimization for Multi-Objective AI Service Orchestration

The proliferation of AI inference services across edge and cloud infrastructures has elevated energy consumption and carbon emissions to critical concerns in service orchestration. On the AI-intensive five-objective formulations we study, conventional decomposition-based multi-objective optimizers exhibit what we term low-energy diversity collapse: neighborhood-restricted updates systematically under-explore the energy-efficient region of the Pareto front spanning inference energy, transport energy, carbon, latency, and reliability. To address this, we propose GENO, a green-aware service orchestration framework built around Decomposition–Dominance Coordination (DDC), a mechanism that synchronizes a dominance archive with the decomposition population via energy-priority redundancy removal and re-association. DDC is complemented by an energy-augmented state encoder, a five-objective formulation that partitions total energy into AI-inference and non-AI components, and a bounded weight-perturbation interface instantiated through a local language model. We establish two formal guarantees: DDC monotonically improves a green-weighted archive score (Proposition 1), and any preference-channel perturbation is Lipschitz-bounded in its effect on the scalarized objective (Proposition 2). Experiments on three service orchestration benchmarks show that, among the eight methods evaluated, GENO is the only one to reach a low-energy operating point (energy-per-request below 0.6 Wh and tail latency under 22 ms) on the AI-EO benchmark, improving energy-per-request over the strongest baseline by roughly a quarter to a third. Component ablation attributes −21.1% HV to DDC removal versus only −2.8% to the language interface, confirming that the gains are algorithmic.