faasHouse: Sustainable serverless edge computing through energy-aware resource scheduling

Serverless edge computing is a specialized system design tailored for Internet of Things (IoT) applications. It leverages serverless computing to minimize operational management and enhance resource efficiency, and utilizes the concept of edge computing to allow code execution near the data sources. However, edge devices powered by renewable energy face challenges due to energy input variability, resulting in imbalances in their operational availability. As a result, high-powered nodes may waste excess energy, while lowpowered nodes may frequently experience unavailability, impacting system sustainability. Addressing this issue requires energy-aware resource schedulers, but existing cloud-native serverless frameworks are energy-agnostic. To overcome this, we propose an energyaware scheduler for sustainable serverless edge systems. We introduce a reference architecture for such systems and formally model energy-aware resource scheduling, treating the function-to-node assignment as an imbalanced energy-minimizing assignment problem. We then design an optimal offline algorithm and propose faasHouse, an online energy-aware scheduling algorithm that utilizes resource sharing through computation offloading. Lastly, we evaluate faasHouse against benchmark algorithms using real-world renewable energy traces and a practical cluster of single-board computers managed by Kubernetes. Our experimental results demonstrate significant improvements in balanced operational availability (by 46%) and throughput (by 44%) compared to the Kubernetes scheduler.