Data-driven Algorithm Selection for Carbon-Aware Scheduling

As computing demand continues to grow, minimizing its environmental impact has become crucial. This paper presents a study on carbon-aware scheduling algorithms, focusing on reducing carbon emissions of delay-tolerant batch workloads. Inspired by the Follow the Leader strategy, we introduce a simple yet efficient meta-algorithm, called FTL, that dynamically selects the most efficient scheduling algorithm based on real-time data and historical performance. Without fine-tuning and parameter optimization, FTL adapts to variability in job lengths, carbon intensity forecasts, and regional energy characteristics, consistently outperforming traditional carbon-aware scheduling algorithms. Through extensive experiments using real-world data traces, FTL achieves 8.2% and 14% improvement in average carbon footprint reduction over the closest runner-up algorithm and the carbon-agnostic algorithm, respectively, demonstrating its efficacy in minimizing carbon emissions across multiple geographical regions. 1