Quantum-Inspired Differential Evolution With Decoding Using Hashing for Efficient User Allocation in Edge Computing Environment

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, due to resources and coverage constraints, edge servers (ESs) in specific areas can only serve a limited number of AUs. Hence, the app user allocation problem (AUAP) becomes challenging in the EC environment. This paper proposes a quantum-inspired differential evolution algorithm (QDE-UA) 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 the minimum use of ES, user allocation rate (UAR), energy consumption, and load balance. Extensive simulations and hypotheses-based statistical analyses (ANOVA, Friedman test) are performed to show the significance of the proposed QDE-UA. The results indicate that QDE-UA outperforms the majority of the existing strategies with an average UAR improvement of 112.42%, and 140.62% enhancement in load balance while utilizing 13.98% fewer ESs. Due to the higher UAR, QDE-UA shows 59.28% higher total energy consumption on average. However, the lower energy consumption per AU is evidence of its energy efficiency.