ABEONA: an Architecture for Energy-Aware Task Migrations from the Edge to the Cloud

TL;DR

This paper introduces ABEONA, an architecture enabling energy-efficient task migration from edge devices to the cloud, demonstrating improved energy efficiency without increasing execution time using artificial and real datasets.

Contribution

The paper proposes ABEONA, a novel edge-to-cloud architecture that facilitates energy-aware task migration, optimizing energy consumption while maintaining performance.

Findings

Energy efficiency improved through task migration

Horizontal scaling at the edge is effective

No negative impact on execution runtime

Abstract

This paper presents our preliminary results with ABEONA, an edge-to-cloud architecture that allows migrating tasks from low-energy, resource-constrained devices on the edge up to the cloud. Our preliminary results on artificial and real world datasets show that it is possible to execute workloads in a more efficient manner energy-wise by scaling horizontally at the edge, without negatively affecting the execution runtime.