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Research on Resource Scheduling Strategy Based on Evolutionary Game in Big Data Environment

Ant Colony Optimization algorithm (ACO) is a kind of probabilistic algorithm used to find the optimal path. It has natural advantages in the resource scheduling algorithm of big data environments. However, the accuracy and stability of the algorithm have yet to be improved because of the slow convergence rate and the ease of convergence to local optima. This paper uses Evolutionary Game Theory (EGT) to optimize the parameters of the ant colony algorithm so that it can be applied to the resource scheduling problem. The parameter optimization method of ant colony algorithm based on evolutionary game theory is used for task scheduling. It is concluded that the pheromone concentration has the highest influencing factors on ant selection path. The total time of the EGT-ACO algorithm in the task scheduling is almost the same as the ACO algorithm, but the completion time of each task is more even, which makes it more fair for each user in resource scheduling.