Cloud Computing Task Scheduling Strategy Based on Improved Ant Colony Optimization (ACO) Algorithm

The rapid development of cloud computing technology has spawned a plethora of complex applications, leading to an increasing demand for efficient task scheduling algorithms. Traditional task scheduling algorithms have certain limitations in resource allocation and load balancing, making it difficult to fully adapt to the dynamic and evolving cloud computing environment. In order to enhance the efficiency of task scheduling, this study investigates a cloud computing task scheduling strategy based on an improved Ant Colony Optimization (ACO) algorithm. By refining the ant colony algorithm, this study proposes specific optimization schemes in three aspects: initialization of pheromones, selection probability of virtual machines, and pheromone updating. Leveraging the strengths of Particle Swarm Optimization (PSO), a hybrid optimization strategy (PSO _ ACO) is constructed. Simulation experiments demonstrate that this improved algorithm exhibits satisfactory performance in terms of task scheduling execution time, load balancing, and resource utilization, offering a novel solution for task management in cloud computing environments.