PACE: Dynamic node consolidation for near-energy-proportionality in edge clusters

Edge computing deployments in remote locations are often powered exclusively by renewable energy sources such as solar panels coupled with batteries. Reducing energy consumption is therefore crucial as excessive power draw may result in depleting the batteries and forcing the system to stop. These settings generate a difficult dilemma in the presence of bursty workloads: on the one hand, edge platforms operate a majority of the time under low-intensity workload, a situation where hardware is the least energy efficient. On the other hand, the system must remain able to deliver high processing capacity during workload peaks. Addressing these two challenges simultaneously mandates the usage of platforms that are as close as possible from energy proportionality. We show in this paper that static provisioning strategies, whether based on powerful monolithic nodes or fixed-sized clusters of Single-Board Computers (SBCs), fail to address these challenges due to high baseline power demand under idle load. Specifically, monolithic nodes have high idle power draw, while static SBC clusters suffer from cumulative idle power across all active nodes, a problem that conventional methods such as Dynamic Voltage and Frequency Scaling (DVFS) cannot solve. We therefore propose Proportional Adaptive Cluster for Edge (PACE), a novel architectural approach using an adaptive SBC cluster governed by a computationally lightweight node consolidation algorithm. PACE’s node consolidation algorithm dynamically powers cluster nodes on or off to precisely match the real workload demand, effectively eliminating the idle power draw of active but unused resources. Through comprehensive performance-to-power characterization and evaluation using real-world traces, we demonstrate that PACE achieves up to a 33% reduction of total energy usage and a 2 × better energy proportionality score compared to static configurations.