Recently, deep learning has emerged as a promising approach for solving challenging resource distribution (RD) problems in vehicle-to-everything (V2X) wireless networks. How-ever, existing neural network architectures lack scalability, in-terpretability, and generalization. To address these limitations, in this study, we propose a new flexible graph neural network (FGNN)-based resource distribution framework for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) user selection and power management in V2X networks with several next- generation access points (APs) and a cluster of V2V and V2I communication users. In the proposed framework, we formu-lated an optimization problem with each V2V and V2I user with the least power constraint that adapts to V2X wireless network settings through training inactive users. Likewise, we consider the situation when every V2I user shares the band with a different group of V2V users. Moreover, we introduce a parameterization of the RD framework strategy employing a flexible graph neural network (FGNN) context derived from instantaneous channel conditions to learn the low-dimension features of every user/vehicle. To assess the execution of the framework, we conduct simulation experiments comparing it with baseline methods in terms of efficiency, sum rate, and fairness.

Cloud computing delivers a desirable environment for users to run their different kinds of applications in a cloud. Numerous of these applications (tasks), such as bioinformatics, astronomy, biodiversity, and image analysis, are deadline-sensitive. Such tasks must be properly allocate to virtual machines (VMs) to avoid deadline violations, and they should reduce their execution time and cost. Due to the contradictory environment, minimizing the application task's completion time and execution cost is extremely difficult. Thus, we propose a Cost-aware Quantum-inspired Genetic Algorithm (CQGA) to minimize the execution time and cost by meeting the deadline constraints. CQGA is motivated by quantum computing and genetic algorithm. It combines quantum operators (measure, interference, and rotation) with genetic operators (selection, crossover, and mutation). Quantum operators are used for better population diversity, quick convergence, time-saving, and robustness. Genetic operators help to produce new individuals, have good fitness values for individuals, and play a significant role in preserving the evolution quality of the population. In addition, CQGA used a quantum bit as a probabilistic representation because it has higher population diversity attributes than other representations. The simulation outcome exhibits that the proposed algorithm can obtain outstanding convergence performance and reduced maximum cost than benchmark algorithms.