A cooperative distributed model to evaluate and optimize task offloading in Mobile Edge Computing

Abstract

This paper proposes a cooperative and distributed framework to evaluate and optimize task offloading in Mobile Edge Computing (MEC). Each agent, representing either a user device or an edge domain, autonomously interacts with others through trust-driven recommendations and cluster formation. The proposed algorithm exploits this information to iteratively increase – and asymptotically converge over time to – the configuration that maximizes the collective utility of edge servers and user devices, i.e., the Average Performance ($AP$), which corresponds to a Nash equilibrium where only reliable agents are rewarded. Two synthetic indicators are introduced to model the main aspects of MEC collaboration: the Quality of Experience ($QoE$), representing the perceived user-side performance, and the Convenience ($C$), expressing the server-side efficiency and resource cost. Experimental validation, performed over a simulated MEC environment with up to 1000 agents, shows a rapid convergence (within 20 iterations), a stable equilibrium with $AP\approx 0.92$, and robustness to variations in the simulated values of agents’ reliability. The results demonstrate that the proposed distributed algorithm achieves efficient, self-organized coordination among heterogeneous edge entities while maintaining scalability and fairness.