Abstract

Modern apps require high computing resources for real-time data processing, allowing app users (AUs) to access real-time information. Edge computing (EC) provides dynamic computing resources to AUs for real-time data processing. However, ESs in specific areas can only serve a limited number of AUs due to resource and coverage constraints. Hence, the app user allocation problem (AUAP) becomes challenging in the EC environment. In this paper, a quantum-inspired differential evolution algorithm (QDE-UA) is proposed for efficient user allocation in the EC environment. The quantum vector is designed to provide a complete solution to the AUAP. The fitness function considers factors such as minimum ES required, user allocation rate (UAR), energy consumption, and load balance. Extensive simulations are performed along with hypotheses-based statistical analyses (ANOVA, Friedman test) to show the significance of the proposed QDE-UA. The results indicate that QDE-UA outperforms existing strategies with an average UAR improvement of 116.63%, a 77.35% reduction in energy consumption, and 46.22% enhancement in load balance while utilizing 13.98% fewer ESs.