Experimenting with Energy-Awareness in Edge-Cloud Containerized Application Orchestration

TL;DR

This paper investigates energy-aware scheduling strategies for edge-cloud applications, demonstrating that incorporating energy metrics into orchestration improves energy efficiency, especially under high load, using real-world ARM device testbeds.

Contribution

It introduces methods to integrate energy metrics into existing scheduling approaches for edge-cloud deployments, enhancing energy efficiency in container orchestration.

Findings

Energy-aware scheduling reduces energy consumption under high load.

Experimental results show improved energy efficiency over standard Kubernetes.

Energy metrics integration benefits heterogeneous edge-cloud infrastructures.

Abstract

This paper explores the role of energy-awareness strategies into the deployment of applications across heterogeneous Edge-Cloud infrastructures. It proposes methods to inject into existing scheduling approaches energy metrics at a computational and network level, to optimize resource allocation and reduce energy consumption. The proposed approach is experimentally evaluated using a real-world testbed based on ARM devices, comparing energy consumption and workload distribution against standard Kubernetes scheduling. Results demonstrate consistent improvements in energy efficiency, particularly under high-load scenarios, highlighting the potential of incorporating energy-awareness into orchestration processes for more sustainable cloud-native computing.