Distributed Offloading in Multi-Access Edge Computing Systems: A Mean-Field Perspective

2025-01-30

AI Summary

To address the challenges of latency sensitivity, energy constraints, and conflicts between resource fairness and priority access in task offloading for massive IoT devices in multi-access edge computing (MEC), this paper introduces mean-field game (MFG) theory into MEC offloading modeling for the first time, establishing a distributed task-decision framework. By leveraging the mean-field approximation, the approach circumvents the information explosion and computational intractability inherent in conventional non-cooperative game formulations, enabling efficient computation of approximate Nash equilibria at scale. The proposed strategy jointly optimizes latency and energy consumption while incorporating a server priority-access mechanism. Numerical experiments demonstrate that it closely approaches centralized optimal performance with significantly reduced communication overhead. Moreover, it explicitly characterizes the Pareto frontier between improved information freshness and reduced terminal energy consumption.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Multi-access edge computing (MEC) technology is a promising solution to assist 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 $N$ devices assisted by one ES, where the devices can split task execution between a local processor and the ES, with equitable access to the ES. In the second scenario, we consider a MEC system consisting of one primary user, $N$ secondary users and one ES. The primary user has priority access to the ES while the secondary users have equitable access 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 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 information exchange overhead between users. Thus, to alleviate such scalability issues, we invoke the paradigm of mean-field games to compute approximate Nash equilibrium policies for each user using their local information, and further 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.

Problem

• Multi-access Edge Computing
• Distributed Task Offloading
• Energy Efficiency and Latency Reduction

Innovation

• Mean Field Game Theory
• Multi-access Edge Computing (MEC)
• Decentralized Task Offloading