Energy-aware DVFS-driven workload provisioning in heterogeneous cloud FaaS architectures

Abstract Cloud Warehouses are evolving with diverse computational resources, including CPUs, GPUs, and accelerators, catering to a multitude of tenant applications. While this heterogeneity promises improved performance and energy efficiency, harnessing its full potential poses challenges due to dynamic workload characteristics and variable application demands. To address this, scheduling approaches combined with optimization techniques like Dynamic Voltage and Frequency Scaling (DVFS) are crucial. However, integrating these approaches effectively can be complex, potentially leading to conflicts and diminished benefits. This research proposes two frameworks, EAPECloud and EAPECloud-DVFS, designed for energy-aware collaborative provisioning in heterogeneous CPU-GPU cloud nodes. The first approach reduces energy consumption by selecting and maintaining a static combination of the best scheduler and V-F pair for most workloads. The second approach goes further by dynamically adjusting the V-F pair of each device using DVFS techniques while selecting the optimal scheduler. While the static approach delivers strong results in most cases, the dynamic strategy achieves even greater energy savings, albeit with an additional convergence time to determine the optimal V-F pair. Although each framework has distinct advantages and use cases, our findings demonstrate that both approaches effectively reduce energy consumption in heterogeneous environments, with EAPECloud-DVFS achieving up to a 126.33% performance improvement compared to the Linux CPU Governor, highlighting its efficiency and applicability in real-time systems.