The traditional Ant Colony Optimization （ACO） algorithm in the process of cloud computing resource scheduling has the defects of unreasonable task and resource node matching， task execution with long time and high cost， unbalanced virtual machine load， and low execution efficiency of the cloud computing system. To address these problems， Ant Colony-Simulated Annealing Algorithm （AC-SAA） was proposed， which aimed to reduce the task execution cost， shorten the task execution time， and keep the system load balanced， and establish the fitness functions of task execution time， cost， and load balancing rate to improve the heuristic factor of the traditional ACO algorithm. The locally optimal solution was solved by the ACO algorithm， and then the solution was further optimized and the pheromone was updated using the simulated annealing algorithm to obtain the globally optimal solution. The proposed algorithm achieved a reasonable allocation of cloud resource nodes and tasks and accelerated the convergence of the algorithm. Experimental results show that compared with the traditional ACO algorithm， AC-SAA shortens the iteration times by at least 52.2%.