基于层次梯度挖掘的数据智能调度算法仿真

Simulation of Data Intelligent Scheduling Algorithm Based on Hierarchical Gradient Mining

针对当前的数据调度算法存在执行费用较高、调度耗时较长且数据资源负载不均衡的问题,提出层次梯度挖掘的数据智能调度算法。采用挖掘主题数据库和层次梯度两者构建层次业务数据库,挖掘数据局部频繁项。根据数据挖掘结果,建立执行时间、执行费用和负载均衡为智能调度目标,构建数据智能调度模型。利用自适应遗传蚁群优化算法对模型求解,最终实现数据智能调度。仿真结果表明:所提算法下数据资源负载结果更均衡,同时还能够有效降低执行费用和调度时长。由此可得结论:本研究具有理想的应用效果。

In this paper,the data intelligent scheduling algorithm based on hierarchical gradient mining was studied in order to reduce the execution cost,shorten the scheduling time and balance the load of data resources.Firstly,the hierarchical business database derived from mining topic database and hierarchical gradient was established to mine local frequent items.Secondly,according to the data mining results,the execution time,execution cost and load balancing were set as the intelligent scheduling objectives in order to build the data intelligent scheduling model.Then,the adaptive genetic ant colony optimization algorithm was introduced to solve the model.Eventually,data intelligent scheduling was achieved.The simulation results show that the algorithm not only reduces the execution cost,shortens the scheduling time,but also balances the load of data resources.