LowCarb: Carbon-Aware Scheduling of Serverless Functions

Serverless computing is observing rapid adoption in cloud computing platforms. Prior works have extensively focused on improving the performance of serverless computing platforms via “keeping alive” functions in memory proactively to lower the function execution latency, but the potential environmental sustainability aspects of such performance-enhancing strategies remain underexplored. This work highlights that serverless computing introduces unique carbon footprint sources and trade-offs between performance and sustainability. We present LowCarb, a novel reinforcement learning-based solution that co-optimizes serverless function performance and carbon footprint. LowCarb effectively quantifies and resolves the inherent conflict between performance and sustainability to achieve results within 15% of optimality.