To address the shortcomings of the standard ant colony algorithm in cloud-computing resource allocation and scheduling, this study proposes an adaptive ant colony algorithm to improve load balance and reduce task execution time and costs. The proposed algorithm can solve tasks submitted by users with a short execution time, low cost, and balanced load rate. The traditional ant colony algorithm, the latest AC-SFL algorithm, and the improved adaptive ant colony algorithm are simulated using the CloudSim platform. Experimental results show that, the improved adaptive ant colony algorithm is able to quickly find a solution for the optimal cloud computing resource scheduling, shorten task completion time, reduce execution cost, and maintain the load balance of the entire cloud system center.