Chen, S., Oikonomou, P., Hua, Z. et al. (4 more authors) (2025) Efficient Placement of Interdependent Services in Multi-access Edge Computing. In: 20th Conference on the Economics of Grids, Clouds, Software, and Services. 20th Conference on the Economics of Grids, Clouds, Software, and Services, 26-27 Sep 2024, Rome, Italy. Springer
Abstract
The rise of 5G fuels multi-access edge computing (MEC), a transformative computing paradigm that leverages edge resources for low-latency mobile access and complex service execution. Deploying services across geographically distributed edge nodes challenges providers to optimize performance metrics like latency and resource efficiency, impacting user experience, operational cost, and environmental footprint. In the context of service scheduling with data flow dependencies, we propose heuristic-based service placement algorithms that balance minimizing latency and maximizing resource efficiency. Our algorithms, evaluated in a simulated environment using state-of-the-art workload benchmarks, achieve significant energy consumption improvements while maintaining comparable latency.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 The Author(s). This is an author produced version of a conference paper accepted for publication in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Service placement; Multi-access edge computing; Task dependency graph |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Distributed Systems & Services |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Sep 2024 10:31 |
Last Modified: | 27 Feb 2025 10:33 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/978-3-031-81226-2_14 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:216837 |
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Filename: Gecon_Paper_2024_SC.pdf
