摘要
测量和评估网络存储系统的性能是用户和企业普遍关心的重点问题之一,因BP神经网络具有强大的非线性映射能力,文中提出了一种利用改进的BP神经网络实现对网络IO性能进行预测的方法。改进的主要内容包括:1)利用马尔科夫链进行预测,更新输出层输出;2)当算法选择概率达到一定值后,利用人工蜂群算法对权值进行优化。最后模拟预测模型的实现过程,将预测结果与传统的BP神经网络进行对比。实验结果证明:该算法能够在基本不增加算法运行时间的情况下提高存储性能预测的求解精度和收敛速度。
Measuring and evaluating the performance of network storage system is one of the key problems to users and corporations.For the strong nonlinear mapping function of the BP-ANN,a new improved algorithm for network I/O performance prediction was proposed by improved BP-ANN,and the new algorithm includes two aspects.Firstly,Mar-kov Chain is used to forecast and update the output of output layer.Secondly,the artificial bee colony algorithm is used to optimize the weights when the probability of algorithm selection reaches a certain value.The implementation process of evaluation model was simulated,and the results were compared with BP-ANN.The experimental results show that the presented approach can significantly improve the solution accuracy and convergence speed of evaluating the performance of network storage system almost without increasing the running time.
作者
郭佳
GUO Jia(School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,China;National Secrecy Science and Technology Evaluation Center,Beijing 100044,China)
出处
《计算机科学》
CSCD
北大核心
2019年第B06期52-55,共4页
Computer Science
