Cui, Kai ; Yilmaz, Mustafa Burak ; Tahir, Anam ; Klein, Anja ; Koeppl, Heinz (2024)
Optimal Offloading Strategies for Edge-Computing via Mean-Field Games and Control.
GLOBECOM 2022 - 2022 IEEE Global Communications Conference. Rio de Janeiro, Brazil (04.12.2022-08.12.2022)
doi: 10.26083/tuprints-00028855
Conference or Workshop Item, Secondary publication, Postprint
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Cui_et_al_2022_Optimal_Offloading_Strategies_for_Edge-Computing_via_Mean-Field_Games_and_Control.pdf Copyright Information: In Copyright. Download (1MB) |
Item Type: | Conference or Workshop Item |
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Type of entry: | Secondary publication |
Title: | Optimal Offloading Strategies for Edge-Computing via Mean-Field Games and Control |
Language: | English |
Date: | 17 December 2024 |
Place of Publication: | Darmstadt |
Year of primary publication: | 2022 |
Place of primary publication: | Piscataway, NJ |
Publisher: | IEEE |
Book Title: | GLOBECOM 2022 - 2022 IEEE Global Communications Conference |
Event Title: | GLOBECOM 2022 - 2022 IEEE Global Communications Conference |
Event Location: | Rio de Janeiro, Brazil |
Event Dates: | 04.12.2022-08.12.2022 |
DOI: | 10.26083/tuprints-00028855 |
Corresponding Links: | |
Origin: | Secondary publication service |
Abstract: | The optimal offloading of tasks in heterogeneous edge-computing scenarios is of great practical interest, both in the selfish and fully cooperative setting. In practice, such systems are typically very large, rendering exact solutions in terms of cooperative optima or Nash equilibria intractable. For this purpose, we adopt a general mean-field formulation in order to solve the competitive and cooperative offloading problems in the limit of infinitely large systems. We give theoretical guarantees for the approximation properties of the limiting solution and solve the resulting mean-field problems numerically. Furthermore, we verify our solutions numerically and find that our approximations are accurate for systems with dozens of edge devices. As a result, we obtain a tractable approach to the design of offloading strategies in large edge-computing scenarios with many users. |
Status: | Postprint |
URN: | urn:nbn:de:tuda-tuprints-288553 |
Classification DDC: | 000 Generalities, computers, information > 004 Computer science 600 Technology, medicine, applied sciences > 621.3 Electrical engineering, electronics |
Divisions: | 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Communications Engineering 18 Department of Electrical Engineering and Information Technology > Self-Organizing Systems Lab |
Date Deposited: | 17 Dec 2024 09:50 |
Last Modified: | 17 Dec 2024 09:51 |
URI: | https://tuprints.ulb.tu-darmstadt.de/id/eprint/28855 |
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