Alkhatani, Nasser, Petri, Ioan  ORCID: https://orcid.org/0000-0002-1625-8247, Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646 and Parashar, Manish
2025.
Edge learning for energy-aware resource management.
Presented at: 2025 IEEE International Conference on Edge Computing and Communications (EDGE),
Helsinki, Finland,
7-12 July 2025.
2025 IEEE International Conference on Edge Computing and Communications (EDGE).
IEEE,
pp. 192-202.
10.1109/edge67623.2025.00030
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Abstract
As the demand for intelligent systems grows, leveraging edge learning and autonomic self-management offers significant benefits for supporting real-time data analysis and resource management in edge environments. We describe and evaluate four distinct task allocation scenarios to demonstrate the autonomics for edge resources management: random execution, autonomic broker-based scheduling, priority-driven execution, and energy-aware allocation. Our experiments reveal that while prioritization-based scheduling minimizes execution times by aligning with task criticality, the energy-aware approach presents a sustainable alternative. This method dynamically adapts task execution based on renewable energy availability, promoting environmentally conscious energy management without compromising operational efficiency. By harnessing renewable energy signals, our findings highlight the potential of edge autonomics to achieve a balance between performance, resource optimization and sustainability. This work demonstrates how intelligent edge-cloud integration can foster resilient smart building infrastructures that meet the challenges of modern computing paradigms.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics Schools > Engineering |
| Publisher: | IEEE |
| ISBN: | 979-8-3315-5559-7 |
| ISSN: | 2767-9918 |
| Date of First Compliant Deposit: | 16 September 2025 |
| Date of Acceptance: | 5 June 2025 |
| Last Modified: | 16 Sep 2025 10:37 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/180650 |
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