Research on Optimization of Cloud Computing Resource Allocation Strategy in Scenic Area Operation and Management under Smart Tourism

Abstract Task scheduling is the core module of smart tourism cloud computing platform. How to make the resource nodes of the scenic cloud computing platform provide services while achieving load balancing effect is the focus of scenic area operation and management. In this paper, ant colony algorithm and fruit fly optimization algorithm are integrated to optimize cloud computing resource allocation in scenic area operation and management. The task scheduling problem is first defined and mathematically modeled, and the distributed computing logic is implemented in MapReduce programming mode. Then for the problems of excessive ineffective search of ant colony algorithm and pheromone increment leading to stagnation, the fruit fly optimization algorithm is introduced, combined with the stability of ant colony optimization algorithm and distributed parallel characteristics, and the FOA-ACA algorithm is designed to solve the situation of the ant colony algorithm’s lack of pheromone concentration at the initial stage and falling into the local optimal solution. In the simulation experiments, the elapsed time of this paper’s algorithm when the number of tasks is 500 is more than 20s lower than the two baseline models, and the degree of load balancing is also better than the baseline model. The experiments fully verify that this paper’s algorithm improves the optimization accuracy of cloud computing resource allocation and the performance of algorithm execution time. This research helps to meet the demand for task time, reliability, cost, etc., and improve the performance and user experience of cloud computing for scenic area operation and management.