[2209.03854] Optimal Offloading Strategies for Edge-Computing via Mean-Field Games and Control
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Computer Science > Distributed, Parallel, and Cluster Computing

arXiv:2209.03854 (cs)
[Submitted on 8 Sep 2022]

Title:Optimal Offloading Strategies for Edge-Computing via Mean-Field Games and Control

Authors:Kai Cui, Mustafa Burak Yilmaz, Anam Tahir, Anja Klein, Heinz Koeppl
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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.
Comments: Accepted to GLOBECOM 2022
Subjects: Distributed, Parallel, and Cluster Computing (cs.DC)
Cite as: arXiv:2209.03854 [cs.DC]
  (or arXiv:2209.03854v1 [cs.DC] for this version)
  https://doi.org/10.48550/arXiv.2209.03854

Submission history

From: Kai Cui [view email]
[v1] Thu, 8 Sep 2022 14:48:38 UTC (858 KB)
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