Dynamic Adaptive User Allocation in Mobile Edge Computing

In mobile edge computing (MEC), mobile users can offload tasks to edge nodes to alleviate local computational loads, leveraging the computing capabilities of edge nodes. However, users' high mobility and temporal variability pose challenges in dynamically allocating mobile users to optimize perceived Quality of Service (QoS). To address this challenge, this paper proposes an adaptive ant colony algorithm for user allocation decisions. This method constructs hidden mobility fitness relationships between users and servers based on user movement trajectories. It utilizes an improved adaptive ant colony algorithm to adjust fitness values automatically and optimize user allocation. The goal is to maximize overall user satisfaction under resource constraints while minimizing user allocation costs. Experimental analysis demonstrates that the proposed method achieves higher user allocation rates and effectively utilizes available resources on edge servers.