GreenAlign: Aligning Mixed LLM Workloads with Intermittent Renewable Supply

Abstract

The increasing use of renewable energy in data centers creates an opportunity to reduce the carbon footprint of energy-intensive LLM inference workloads. Unlike traditional stable power supply, renewable generation fluctuates over time, making it difficult to match computation demand with available energy. However, existing LLM serving systems primarily optimize latency and throughput without considering energy supply dynamics, leading to underutilization of renewable energy and unnecessary reliance on thermal power, and consequently, higher carbon emissions. We present GreenAlign, a renewable-aware scheduling framework that addresses this mismatch by treating best-effort (BE) requests as temporally shiftable load. GreenAlign enforces a power-constrained policy that executes BE requests using only residual renewable energy under normal conditions, and introduces a backlog risk metric to selectively relax this constraint when deadline violations are imminent. To ensure responsiveness, it maintains standby capacity to absorb unpredictable latencycritical (LC) bursts and uses lightweight length estimation to handle request uncertainty. Simulation results show that GreenAlign significantly reduces thermal energy usage while preserving LC latency and BE deadline satisfaction.