Beyond the Accelerator: Thermodynamic-Aware Orchestration for SDG 17 Federated Infrastructure

System: Genesis Conductor / TAO Controller
Series: Thermodynamic-Aware Orchestration (TAO)

Abstract:
The escalating energy demands of production AI systems are increasingly constrained not by model efficiency, but by orchestration overhead. We introduce Optimization Inversion: an empirically observed regime where infrastructure overhead dominates total energy consumption. To address this, we present Thermodynamic-Aware Orchestration (TAO), a closed-loop control architecture that enforces energy constraints as first-class scheduling inputs. TAO provides a technical foundation for SDG 17: Partnerships for the Goals by enabling multi-stakeholder collaboration through open scholarly infrastructure. By integrating hardware telemetry with Landauer-inspired penalty functions, TAO ensures interoperability across federated compute environments. This work demonstrates a 74.8% reduction in power demand while emitting auditable governance artifacts compliant with the EU AI Act, fostering a transparent and sustainable ecosystem for global AI deployment.

TECHNICAL KEYWORDS:
SDG 17, Open Scholarly Infrastructure, Thermodynamic-Aware Orchestration, Optimization Inversion, Energy-Aware AI, EU AI Act Compliance, Green AI, Pareto Optimization, RAPL, NVML, eBPF, Kubernetes Orchestration.