University of Oulu

J. L. Thomsen et al., "Edge Computing Tasks Orchestration: An Energy-Aware Approach," 2023 IEEE International Conference on Edge Computing and Communications (EDGE), Chicago, IL, USA, 2023, pp. 115-117, doi: 10.1109/EDGE60047.2023.00027

Edge computing tasks orchestration : an energy-aware approach

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Author: Thomsen, Johan Løhde1; Dragsbæk Schmidt Thomsen, Kristian1; Schmidt, Rasmus B.1;
Organizations: 1Aalborg University, Denmark
2Tampere University, Tampere, Finland
3University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.2 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2023
Publish Date: 2023-11-03


In this paper, we investigate experimentally the use of auctioning as a method for optimizing task orchestration in distributed computing systems by making selfish agents compete to execute computational tasks. Our goal is to find an approach that can improve the performance of these systems, using a deadline, fines, and reward limits in a reverse second-price sealed bid auction, to incentive and control the system, specifically in terms of improving task throughput and power consumption. With improvements to both energy consumption and task throughput, we have developed a promising approach, that is able to scale with the number of machines in the system. Results suggest that this type of auction may be useful for improving the implementation of these systems in a wide range of scenarios.

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Series: IEEE International Conference on Edge Computing
ISSN: 2767-990X
ISSN-E: 2767-9918
ISSN-L: 2767-990X
ISBN: 979-8-3503-0483-1
ISBN Print: 979-8-3503-0484-8
Pages: 115 - 117
DOI: 10.1109/EDGE60047.2023.00027
Host publication: 2023 IEEE International Conference On Edge Computing & Communications, IEEE EDGE 2023 : proceedings
Host publication editor: Ardagna, Claudio
Awaysheh, Feras
Bian, Hongyi
Chang, Carl K.
Chang, Rong N.
Delicato, Flavia
Desai, Nirmit
Fan, Jing
Fox, Geoffrey C.
Goscinski, Andrzej
Jin, Zhi
Kobusińska, Anna
Rana, Omer
Conference: IEEE International Conference on Edge Computing & Communications
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Funding: This work was partially supported by the Validation WP of the ERC Advanced Grant LASSO (Learning, Analysis, SynthesiS and Optimization of Cyber-Physical Systems), by Industry X and 6GSoft projects funded by Business Finland.
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