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

TL;DR

This paper proposes a mean-field game approach to optimize task offloading in multi-access edge computing systems, addressing scalability and latency issues for large user populations with equitable and priority access scenarios.

Contribution

It introduces a scalable mean-field game framework for offloading in MEC systems, capturing priority and equitable access, and analyzing the trade-offs between information freshness and power consumption.

Findings

The mean-field approach approximates centralized solutions effectively.

Optimal policies balance power consumption and information freshness.

The framework provides insights into offloading behavior under different access scenarios.

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.