A Multi-Objective Cloud Computing Task Scheduling Study Based On Improved Genetic Algorithm

With the continuous popularization of cloud computing technologies,the problem of unbalanced load on the cloud has become increasingly prominent.It is extremely urgent to solve this problem of load imbalance.The task scheduling technology in cloud provides a highly efficient and convenient solution that allocates appropriate tasks to each computing node in the cloud in real time,thereby alleviating the problem of unbalanced load to a certain extent and improving the calculation of the cloud platform and storage capabilities.This paper summarizes the current research status of domestic and foreign scholars in the field of task scheduling in cloud computing,and proposes an improved multi-objective optimization genetic algorithm based on NSGA-II algorithm.This algorithm introduces crossover operators based on an improved probability model and a mutation operator based on individual evaluation.The improved crossover operator can be well applied to integer coding and maintain the advantages of double parental chromosomes,and keep the diversity of populations;improved mutation operators can increase the local search capability of the algorithm.In the simulation experiment stage,the task scheduling simulation using CloudSim shows that the improved NSGA-:II algorithm is an efficient and reliable cloud computing task scheduling algorithm.