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Distributed Offloading in Multi-Access Edge Computing Systems: A Mean-Field Perspective
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DOI：10.1109/TMC.2025.3642928.png)
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Abstract
En 中文
With the widespread adoption of internet-of-things (IoT) devices capable of supporting numerous intelligent applications, the demand for computational power has surged dramatically. Multi-access edge computing (MEC) technology is a promising solution to assist the often power-constrained IoT devices by providing additional computing resources for time-sensitive tasks. In this paper, we consider the problem of optimal task offloading in MEC systems with due consideration of the timeliness and scalability issues under two scenarios of equitable and priority access to the edge server (ES). In the first scenario, we consider a MEC system consisting of <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> devices assisted by one ES, where the devices can split task execution between a local processor and the ES, with <i>equitable access</i> to the ES. In the second scenario, we consider a MEC system consisting of one primary user, <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> secondary users and one ES. The primary user has <i>priority access</i> to the ES while the secondary users have <i>equitable access</i> to the ES amongst themselves. In both scenarios, due to the power consumption associated with utilizing the local resource and task offloading, the devices must optimize their actions. Additionally, since the ES is a shared resource, other users’ offloading activity serves to increase latency incurred by each user. We thus model both scenarios using a <i>large user</i> non-cooperative game framework. However, the presence of a large number of users makes it nearly impossible to compute the equilibrium offloading policies for each user, which would require a significant communication overhead to exchange information with each other. Thus, to alleviate such scalability issues, we invoke the paradigm of mean-field games (MFGs) to design completely distributed low complexity algorithms for the computation of approximate Nash equilibrium policies for each user based on only their local information. Further, by leveraging the novel age of information (AoI) metric, we study the trade-offs between increasing information freshness and reducing power consumption for each user. Using numerical evaluations, we show that our approach can recover the offloading trends displayed under centralized solutions, and provide additional insights into the results obtained.
Keywords:
Multi-access edge computing
mean-field games
age of information
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