GAS: DVFS-Driven Energy Efficiency Approach for Latency-Guaranteed Edge Computing Microservices

Abstract

Edge computing-based microservices (ECM) are pivotal infrastructure components for latency-critical applications such as Virtual Reality/Augmented Reality (VR/AR) and the Internet of Things (IoT). ECM involves strategically deploying microservices at the network’s edge to fulfill the low latency needs of modern applications. However, achieving efficient resource and energy consumption while meeting the latency requirement in the ECM environment remains challenging. Dynamic Voltage and Frequency Scaling (DVFS) is a common technique to address this issue. It adjusts the CPU frequency and voltage to balance energy cost and performance. However, selecting the optimal CPU frequency depends on the nature of the microservice workload (e.g., CPU-bound, memory-bound, or mixed). Moreover, various microservices with different latency requirement can be deployed on the same edge node. This makes the DVFS application extremely challenging, particularly for a chip-wide DVFS implementation for which CPU cores operate at the same frequency and voltage. To this end, we propose GAS, enerGy Aware microServices edge computing framework, which enables CPU frequency scaling to meet diverse microservice latency requirement with the minimum energy cost. Our evaluation indicates that our CPU scaling policy decreases energy consumption by 5% to 23% compared to Linux governors while maintaining latency requirement and significantly contributing to sustainable edge computing.