Simulation study demonstrates improved task scheduling efficiency in cloud computing environments, highlighting the value of rule-constrained genetic algorithms.
In the vigorous development era of Internet-based applications, the demand for cloud computing is getting higher and higher. To solve the task scheduling in cloud computing, we propose a task scheduling model based on genetic algorithm. Gaussian mutation is to improve traditional genetic algorithms to enhance the adaption to the change of our scheduling model. Quick sort and optimal selection introduce to improve fitness function, making screening faster and more convenient. The crossover and mutation operations with rule constraints are use to improve the quality of individuals. The results of simulation experiments show that the improved algorithm can solve the task scheduling problem in cloud computing environment more effectively.
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Jinglin He (2024) studied this question.
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