Energy-Aware Resource Scheduling for Serverless Edge Computing

Abstract

In this paper, we present energy-aware scheduling for Serverless edge computing. Energy awareness is critical since edge nodes, in many Internet of Things (IoT) domains, are meant to be powered by renewable energy sources that are variable, making low-powered and/or overloaded (bottleneck) nodes unavailable and not operating their services. This awareness is also required since energy challenges have not been previously addressed by Serverless, largely due to its origin in cloud computing. To achieve this, we formally model an energy-aware resource scheduling problem in Serverless edge computing, given a cluster of battery-operated and renewable-energy powered nodes. Then, we devise zone-oriented and priority-based algorithms to improve the operational availability of bottleneck nodes. As assets, our algorithm coins terms "sticky offloading" and "warm scheduling" in the interest of the Quality of Service (QoS). We evaluate our proposal against well-known benchmarks using real-world implementations on a cluster of Raspberry Pis enabled with container orchestration, Kubernetes, and Serverless computing, OpenFaaS, where edge nodes are powered by real-world solar irradiation. Experimental results achieve significant improvements, up to 33%, in helping bottleneck node's operational availability while preserving the QoS. With energy awareness, now Serverless can unconditionally offer its resource efficiency and portability at the edge.