A Study of Task Scheduling Based on Differential Evolution Algorithm in Cloud Computing

In this paper, we put forward a task scheduling algorithm in cloud computing with the goal of the minimum completion time, maximum load balancing degree, and the minimum energy consumption using improved differential evolution algorithm. In order to improve the global search ability in the earlier stage and the local search ability in the later stage, we have adopted the adaptive zooming factor mutation strategy and adaptive crossover factor increasing strategy. At the same time, we have strengthened the selection mechanism to keep the diversity of population in the later stage. In the process of simulation, we have performed the functional verification of the algorithm and compared with the other representative algorithms. The experimental results show that the improved differential evolution algorithm can optimize cloud computing task scheduling problems in task completion time, load balancing, and energy efficient optimization.