计算机科学 ›› 2019, Vol. 46 ›› Issue (6A): 52-55.

• 智能计算 • 上一篇    下一篇

基于改进的人工神经网络对存储系统性能进行预测的方法

郭佳   

  1. 北京交通大学计算机与信息技术学院 北京100044;
    国家保密科技测评中心 北京100044
  • 出版日期:2019-06-14 发布日期:2019-07-02
  • 通讯作者: 郭 佳(1983-),女,博士生,工程师,主要研究方向为信息安全,E-mail:m13581902161@163.com(通信作者)。

Method of Predicting Performance of Storage System Based on Improved Artificial Neural Network

GUO Jia   

  1. School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,China;
    National Secrecy Science and Technology Evaluation Center,Beijing 100044,China
  • Online:2019-06-14 Published:2019-07-02

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。

关键词: BP神经网络, 存储系统, 马尔科夫链, 人工蜂群算法

Abstract: 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.

Key words: ABC, BP-ANN, Markov chain, Storage systems

中图分类号: 

  • TP389
[1]崔宝江,刘军,王刚,等.网络存储系统 I/O 响应时间边界性能研究[J].通信学报,2006,27(1):69-74.
[2]陈琼,郑启伦,凌卫新.采用计数器存储权值的人工神经网络的实现[J].计算机工程与应用,2001,20:22-25.
[3]KARABOGA D.An idea based on honey bee swarm for numerical optimization[R].Erciyes University,Kayseri,Turkey,TechnicalReport-TR06,2005.
[4]KARABOGA D,AKAY B,OZTURK C.Artificial Bee Colony (ABC) optimization algotithm for training feed-forward neural networks[C]∥LNCS:Modeling Decisions for Artificial Intelligence.Springer-Verlag,2007:18-329.
[5]BEATRIZ A G,HUMBERTO S,ROBERTO A.VÁZQUEZ.Artificial neural network synthesis by means of artificial bee colony (ABC) algorithm[C]∥2011 IEEE Congress of Evolutionary Computation (CEC).2011:331-338.
[6]暴励.人工蜂群算法的混合策略研究[D].太原:太原科技大学,2010.
[7]ZHU G,KWONG S.Gbest-Guided artificial bee colony algo-rithm for numerical function optimization[J].Applied mathematics and Computation,2010,217(7):3166-3173.
[8]周新宇,吴志健,王文明.基于正交实验设计的人工蜂群算法[J].软件学报,2015,26(9):2167-2190.
[9]冷昕,张树群,雷兆宜.改进的人工蜂群算法在神经网络中的应用[J].计算机工程与应用,2016,52(11):7-10.
[10]王允霞.蜂群算法的研究及其在人工神经网络中的应用[D].广州:华南理工大学,2013:25-27.
[11]向万里,马寿峰.基于轮盘赌反向选择机制的蜂群优化算法[J].计算机应用研究,2013(1):86-89.
[12]魏波,喻飞,徐星,等.基于改进轮盘赌策略的交互式演化算法[J].计算机与数字工程,2014(10):1762-1767.
[13]ROMANOVSKII V.Discrete Markov’s chains[M].Moscow:Gostexizdat,1949.
[14]WHITTAKER J A,THOMASON M G.A Markov Chain Mo-del for Statistical software testing[J].IEEE Transactions on Software Engineering,1994,30(10):812-824.
[15]MAREK I,SZYLD D B.Algebraic schwarz methods for the numerical solution of Markov chains[J].Linar Algebraic and its Applications,2004,386:67-81.
[16]POGGI P,NOTTON G,MUSELLI M.Stochastic study of hourly total solar radiation in Corsica using a Markov model[J].International Journal of Climatology,2000,20(14):1843-1860.
[17]LI Y Z,LUAN R,NIU J C.Forecast of power generation for grid-connected photovoltaic system based on grey model and Markov chain[C]∥3rd IEEE Conference on Industrial Electronics and Applications.Singapore:IEEE,2008:1729-1733.
[18]刁莹.用数学建模方法评价存储系统性能[D].哈尔滨:哈尔滨工程大学,2013:82-88.
[19]林已杰.一种基于马尔科夫和神经网络的软件衰退预测方法研究[D].重庆:西南大学,2010:31-32.
[20]KARABOGA D,GORKEMLI B,OZTURK C,Karaboga N.A comprehensive survey:Artificial bee colony (ABC) algorithm and applications[J].Artificial Intelligence Review,2014,42(1):21-57.
[1] 刘宝宝, 杨菁菁, 陶露, 王贺应.
基于DE-LSTM模型的教育统计数据预测研究
Study on Prediction of Educational Statistical Data Based on DE-LSTM Model
计算机科学, 2022, 49(6A): 261-266. https://doi.org/10.11896/jsjkx.220300120
[2] 徐佳楠, 张天瑞, 赵伟博, 贾泽轩.
面向供应链风险评估的改进BP小波神经网络研究
Study on Improved BP Wavelet Neural Network for Supply Chain Risk Assessment
计算机科学, 2022, 49(6A): 654-660. https://doi.org/10.11896/jsjkx.210800049
[3] 朱旭辉, 沈国娇, 夏平凡, 倪志伟.
基于螺旋进化萤火虫算法和BP神经网络的模型及其在PPP融资风险预测中的应用
Model Based on Spirally Evolution Glowworm Swarm Optimization and Back Propagation Neural Network and Its Application in PPP Financing Risk Prediction
计算机科学, 2022, 49(6A): 667-674. https://doi.org/10.11896/jsjkx.210800088
[4] 夏静, 马中, 戴新发, 胡哲琨.
基于BP神经网络的智能云效能模型
Efficiency Model of Intelligent Cloud Based on BP Neural Network
计算机科学, 2022, 49(2): 353-367. https://doi.org/10.11896/jsjkx.201100140
[5] 石克翔, 保利勇, 丁洪伟, 官铮, 赵雷.
基于生成时间序列均匀优化的混沌人工蜂群算法
Chaos Artificial Bee Colony Algorithm Based on Homogenizing Optimization of Generated Time Series
计算机科学, 2021, 48(7): 270-280. https://doi.org/10.11896/jsjkx.200800087
[6] 程铁军, 王曼.
基于变权组合的突发事件网络舆情趋势预测
Network Public Opinion Trend Prediction of Emergencies Based on Variable Weight Combination
计算机科学, 2021, 48(6A): 190-195. https://doi.org/10.11896/jsjkx.200600094
[7] 郭福民, 张华, 胡瑢华, 宋岩.
一种基于表面肌电信号的腕部肌力估计方法研究
Study on Method for Estimating Wrist Muscle Force Based on Surface EMG Signals
计算机科学, 2021, 48(6A): 317-320. https://doi.org/10.11896/jsjkx.200600021
[8] 张航, 唐聃, 蔡红亮.
分布式存储系统中的预测式纠删码研究
Study on Predictive Erasure Codes in Distributed Storage System
计算机科学, 2021, 48(5): 130-139. https://doi.org/10.11896/jsjkx.200300124
[9] 张晓, 张思蒙, 石佳, 董聪, 李战怀.
Ceph分布式存储系统性能优化技术研究综述
Review on Performance Optimization of Ceph Distributed Storage System
计算机科学, 2021, 48(2): 1-12. https://doi.org/10.11896/jsjkx.201000149
[10] 石琳姗, 马创, 杨云, 靳敏.
基于SSC-BP神经网络的异常检测算法
Anomaly Detection Algorithm Based on SSC-BP Neural Network
计算机科学, 2021, 48(12): 357-363. https://doi.org/10.11896/jsjkx.201000086
[11] 焦东来, 王浩翔, 吕海洋, 徐轲.
基于手机传感器轨迹的路面地物检测方法
Road Surface Object Detection from Mobile Phone Based Sensor Trajectories
计算机科学, 2021, 48(11A): 283-289. https://doi.org/10.11896/jsjkx.210200145
[12] 周俊, 尹悦, 夏斌.
基于LSTM神经网络的声发射信号识别研究
Acoustic Emission Signal Recognition Based on Long Short Time Memory Neural Network
计算机科学, 2021, 48(11A): 319-326. https://doi.org/10.11896/jsjkx.210700034
[13] 郑友莲, 雷德明, 郑巧仙.
求解高维多目标调度的新型人工蜂群算法
Novel Artificial Bee Colony Algorithm for Solving Many-objective Scheduling
计算机科学, 2020, 47(7): 186-191. https://doi.org/10.11896/jsjkx.190600089
[14] 周立鹏, 孟利民, 周磊, 蒋维, 董建平.
基于BP神经网络的摔倒检测算法
Fall Detection Algorithm Based on BP Neural Network
计算机科学, 2020, 47(6A): 242-246. https://doi.org/10.11896/JsJkx.191000077
[15] 诸珺文.
基于改进BP神经网络的SQL注入识别
SQL InJection Recognition Based on Improved BP Neural Network
计算机科学, 2020, 47(6A): 352-359. https://doi.org/10.11896/JsJkx.191200054
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!