A Resource Allocation Method for Big Data Centers Based on Improved Genetic Algorithm
In order to improve the resource allocation efficiency of big data centers in processing massive data requests and reduce operating costs,this study adopts an improved genetic algorithm to optimize the resource allocation process.Considering the shortcomings of traditional genetic algorithms in large-scale systems,such as slow convergence speed and local optimum problems,this study introduces adaptive mutation rate and elite selection strategy to enhance the algorithm's search ability and adaptability.This study also designed a new fitness function to more accurately reflect resource allocation efficiency and meet fairness requirements,designed a resource allocation model for big data centers based on improved genetic algorithms,achieving dynamic sorting and efficient allocation of resources in big data centers.Through simulation testing,the performance of the improved genetic algorithm in resource utilization,response time,energy consumption,and other aspects has been verified.The results show that compared with traditional genetic algorithms,the resource allocation method for big data centers based on improved genetic algorithms has significantly improved in various performance indicators,and can support the adaptability requirements of big data centers in the face of constantly changing workloads and operating environments.
improve genetic algorithmbig data centerresource allocationfitness function
万方数据
维普
