一种面向云计算群组优化时效改进的学习模型

Improved Learning Model for Cloud Computing Swarm Optimization Time Efficiency

针对传统的云计算调度模型对任务调度求解时间长的缺陷,提出一种结合差分进化的改进的新蝙蝠算法(Optimized Novel Bat Algorithm,ONBA)优化算法来获取任务的调度数据。利用该调度数据对改进的改进的深度信念网络(Improved Deep Belief Network,IDBN)模型进行训练,通过对训练学习率和训练次数的自适应调优来实现训练时效的提高,从而实现对云计算调度结果的快速准确预测。实验结果表明,应用该方法训练完成的改进IDBN模型进行调度时,在保证预测群组优化结果准确的前提下,其能够有效缩短云计算的实际调度时间,弥补了传统群组优化模型调度耗时的缺陷。

Aiming at the time-consuming problem when the traditional task scheduling models of cloud computing deal with the tasks,this paper proposed an ONBA algorithm combining DE(Differential Evolution)to get the scheduling data of task.Then,the obtained scheduling data are used to train the improved IDBN model.By adjusting the learning rate and training times,the time efficiency can be improved,thus achieving fast and accurate prediction of cloud computing scheduling results.The experimental results show that the improved IDBN model trained by this method can effectively shorten the actual scheduling time on the premise of ensuring precise prediction results and make up for the defect of long running time in traditional swarm optimization models.