Carbon-Aware Cross-Cloud Scheduling for LLM and Grid Analytics

Cross-cloud artificial intelligence platforms increasingly serve both flexible large-language-model (LLM) workflows and time-critical power-grid analytics. Carbon-aware scheduling can shift model work toward lower-emission regions and times, but indiscriminate shifting can delay phasor measurement unit (PMU) event detection, lose calibration warnings, violate data-location policy, or make model outputs unavailable when operators need them. This paper proposes CACS, a Carbon-Aware Cross-Cloud Scheduler for LLM and grid analytics. CACS fuses forecast carbon intensity, cloud capacity and latency telemetry, governed API intents, PMU digital-twin state, calibration risk, and job-specific service guarantees. It admits or routes strict grid jobs immediately through feasible low-latency paths while spatially or temporally shifting deferrable LLM inference, retrieval refresh, and replay analysis under explicit deadline and policy constraints. A simulated 30-day benchmark with three cloud regions, two grid edge zones, and mixed PMU and LLM traffic shows that CACS reduces estimated operational emissions by 31.6% compared with latency-first approved-cloud routing while retaining a 242 ms P95 urgent-grid decision latency, 98.8% deadline satisfaction, and zero simulated policy violations.