基于改进遗传算法的大数据中心资源分配方法

A Resource Allocation Method for Big Data Centers Based on Improved Genetic Algorithm

为提高大数据中心在处理海量数据请求时的资源分配效率,降低运营成本,本研究采用改进的遗传算法优化资源分配过程。考虑到传统遗传算法在大规模系统中应用时存在的不足,如收敛速度慢、局部最优问题,本研究引入自适应变异率与精英选择策略增强算法搜索能力与适应性。本研究还设计了一种新适应度函数,以更精确地反映资源分配效率,满足公平性需求;进行了基于改进遗传算法的大数据中心资源分配模型设计,实现了大数据中心资源动态排序与高效分配。通过模拟测试,验证了改进遗传算法在资源利用率、响应时间、能耗等方面的表现。结果表明,与传统遗传算法相比,基于改进遗传算法的大数据中心资源分配方法在各项性能指标上都有显著提升,可支持大数据中心面对不断变化的工作负载和运行环境的适应性需求。

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.