A Profit-Aware Double-Layer Edge Equipment Deployment Approach for Cloud Operators in Multi-Access Edge Computing

Multi-access edge computing (MEC) is an emerging computing architecture that enhances and extends traditional mobile cloud computing. As a new architecture, MEC brings many advantages but faces some challenges as well. The deployment of edge equipment (EE) is a fundamental issue in building MEC, and is also the primary challenge for cloud operators (COs). This paper proposes a new EE deployment approach that includes a double -layer deployment model and a double -layer policy algorithm for COs in MEC to optimize the CO profit. The deployment problem is decomposed into double sub -problems, and formulated as a groupstyle constrained double -layer optimization model with the goal of maximizing the CO profit while satisfying the delay constraint. To solve the deployment model, an adaptive group genetic algorithm -based double -layer policy algorithm has been designed. The simulation results show that low electricity charges, long tolerable delays, energy -efficient EE and high rental resource charges contribute to increasing the CO profit. Our policy algorithm outperforms the existing algorithms, and achieves at least a 12.74% increment in the CO profit.