Decentralized Task Offloading and Load-Balancing for Mobile Edge Computing in Dense Networks

TL;DR

This paper proposes a decentralized approach combining mean field multi-agent bandit algorithms with load-balancing techniques to optimize task offloading and resource distribution in dense mobile edge networks.

Contribution

It introduces a novel method that integrates multi-agent bandit learning with load balancing to achieve desired server load profiles without centralized control.

Findings

Effective convergence to target load distribution

Demonstrated scalability in dense networks

Improved load balancing performance

Abstract

We study the problem of decentralized task offloading and load-balancing in a dense network with numerous devices and a set of edge servers. Solving this problem optimally is complicated due to the unknown network information and random task sizes. The shared network resources also influence the users' decisions and resource distribution. Our solution combines the mean field multi-agent multi-armed bandit (MAB) game with a load-balancing technique that adjusts the servers' rewards to achieve a target population profile despite the distributed user decision-making. Numerical results demonstrate the efficacy of our approach and the convergence to the target load distribution.