ABEONA: an Architecture for Energy-Aware Task Migrations from the Edge to the Cloud
Isabelly Rocha, Gabriel Vinha, Andrey Brito, Pascal Felber, Marcelo, Pasin, Valerio Schiavoni

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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
