基于稳态过程的多重分形Web日志仿真生成算法

doi:10.11772/j.issn.1001-9081.2017.02.0587

计算机应用 ›› 2017, Vol. 37 ›› Issue (2): 587-592.DOI: 10.11772/j.issn.1001-9081.2017.02.0587

基于稳态过程的多重分形Web日志仿真生成算法

彭行雄^1,2, 肖如良^1,2

1. 福建师范大学软件学院, 福州 350117;
2. 福建省公共服务大数据挖掘与应用工程技术研究中心, 福州 350117

收稿日期:2016-06-14 修回日期:2016-08-18 出版日期:2017-02-10 发布日期:2017-02-11
通讯作者: 肖如良,xiaoruliang@163.com
作者简介:彭行雄(1991-),男,湖北孝感人,硕士研究生,主要研究方向:机器学习;肖如良(1966-),男,湖南娄底人,教授,博士,CCF高级会员,主要研究方向:软件工程、大数据软件新技术。
基金资助:
福建省高校产学合作项目（2016H6007）。

Multi-fractal Web log simulation generation algorithm based on stable process

PENG Xingxiong^1,2, XIAO Ruliang^1,2

1. Faculty of Software, Fujian Normal University, Fuzhou Fujian 350117, China;
2. Fujian Provincial Engineering Research Center of Public Service Big Data Analysis and Application, Fuzhou Fujian 350117, China

Received:2016-06-14 Revised:2016-08-18 Online:2017-02-10 Published:2017-02-11
Supported by:
This work is partially supported by the Fujian Provincial Great Plan Project (2016H6007).

摘要/Abstract

摘要： 运行在服务器集群的软件系统需要Web日志的大规模数据集以满足性能测试的需求，但现有仿真生成算法因模型单一而无法满足要求。针对此问题，提出一种基于alpha稳态过程的多分形Web日志的仿真生成算法。首先，在长相关尺度（LRD）下采用alpha稳态过程来描述Web日志的自相似性；其次，在短相关尺度（RSD）下采用二项式b模型描述Web日志的多重分形性；最后，将长相关模型和短相关模型融合于改进的ON/OFF框架中。与单一的模型相比，新算法的参数物理意义明确，具有良好的自相似性和多分形性。实验结果表明，该算法能够较准确地模拟真实Web日志，可以有效地应用于Web日志大规模数据集的仿真生成。

关键词: 稳态过程, 多重分形, 自相似, 时间序列, 日志分析, 仿真生成

Abstract: The software system running on the server cluster needs large-scale data sets of Web log to meet the performance test requirement, but the existing simulation generation algorithm cannot meet the requirements due to the single model. Aiming at this problem, a new multi-fractal Web log simulation generation algorithm based on alpha stable process was proposed. Firstly, the self-similarity of Web log was described by alpha stable process in Long Range Dependence (LRD). Secondly, the multi-fractal of Web log was described by binomial-b model in Short Range Dependence (SRD). Finally, the model of long range dependence and the model of short range dependence were integrated into the improved ON/OFF framework. Compared with the single model, the parameters of the proposed algorithm has clear physical meaning equipped with good performance of self-similarity and multi-fractal. The experimental results show that the proposed algorithm can accurately simulate the real Web log and be effectively applied in Web log simulation generation with large-scale data sets.

Key words: stable process, multi-fractal, self-similarity, time series, log analysis, simulation generation

中图分类号:

TP311

彭行雄, 肖如良. 基于稳态过程的多重分形Web日志仿真生成算法[J]. 计算机应用, 2017, 37(2): 587-592.

PENG Xingxiong, XIAO Ruliang. Multi-fractal Web log simulation generation algorithm based on stable process[J]. Journal of Computer Applications, 2017, 37(2): 587-592.

参考文献

[1] CALZAROSSA M C, MASSARI L, TESSERA D. Workload characterization:a survey revisited[J]. ACM Computing Surveys, 2016, 48(3):Article No. 48.
[2] CROVELLA M E, BESTAVROS A. Self-similarity in World Wide Web traffic:evidence and possible causes[J]. IEEE/ACM Transactions on Networking, 1997, 5(6):835-846.
[3] SARLA P, DOODIPALA M R, DINGARI M. Self-similarity analysis of Web users arrival pattern at selected Web centers[J]. American Journal of Computational Mathematics, 2016, 6(1):17-22.
[4] RIEDI R H, CROUSE M S, RIBEIRO V J, et al. A multifractal wavelet model with application to network traffic[J]. IEEE Transactions on Information Theory, 1999, 45(3):992-1018.
[5] 张雪媛,王永刚,张琼.基于分数布朗运动的自相似流量判别及生成方法[J].计算机应用,2013,33(4):947-949,963. (ZHANG X Y, WANG Y G, ZHANG Q. Self-similar traffic discrimination and generating methods based on fractal Brown motion[J]. Journal of Computer Applications, 2013, 33(4):947-949, 963.).
[6] GOMEZ M E, SANTONJA V. Analysis of self-similarity in I/O workload using structural modeling[C]//Proceedings of the 19997th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems. Piscataway, NJ:IEEE, 1999:234.
[7] LELAND W E, TAQQU M S, WILLINGER W, et al. On the self-similar nature of Ethernet traffic[C]//SIGCOMM'93:Proceedings of the 1993 Conference proceedings on Communications Architectures, Protocols and Applications. New York:ACM, 1993:183-193.
[8] WANG M Z, MADHYASTHA T, CHAN N H, et al. Data mining meets performance evaluation:fast algorithms for modeling bursty traffic[C]//ICDE'02:Proceedings of the 18th International Conference on Data Engineering. Washington, DC:IEEE Computer Society, 2002:507.
[9] HONG B, MADHYASTHA T M. The relevance of long-range dependence in disk traffic and implications for trace synthesis[C]//Proceedings of the 22nd IEEE/13th NASA Goddard Conference on Mass Storage Systems and Technologies. Piscataway, NJ:IEEE, 2005:316-326.
[10] WEN J, MA Y, LIU P, et al. Distributed multipliers in MWM for analyzing job arrival processes in massive HPC workload datasets[J]. Future Generation Computer Systems, 2014, 37(7):335-344.
[11] GUO L, TAN E, CHEN S, et al. The stretched exponential distribution of Internet media access patterns[C]//PODC'08:Proceedings of the Twenty-Seventh ACM Symposium on Principles of Distributed Computing. New York:ACM, 2008:283-294.
[12] CHEN S, GHORBANI M, WANG Y Z, et al. Trace-based analysis and prediction of cloud computing user behavior using the fractal modeling technique[C]//Proceedings of the 2014 IEEE International Congress on Big Data (BigData Congress). Piscataway, NJ:IEEE, 2014:733-739.
[13] NOLAN J P. Stable distributions:models for heavy tailed data[EB/OL].[2016-01-30]. https://www.researchgate.net/publication/247635151_Stable_Distribution_Models_for_Heavy-Tailed_data.
[14] 邹强,程强.存储系统负载自相似性研究综述[J].计算机科学,2013,40(3):24-30. (ZOU Q, CHENG Q. Survey of studies on self-similarity in storage system workload[J]. Computer Science, 2013, 40(3):24-30.).
[15] DRǍGULESCU B A, YAKOVENKO V M. Exponential and power-law probability distributions of wealth and income in the United Kingdom and the United States[J]. Physica A:Statistical Mechanics and Its Applications, 2001, 299(1/2):213-221.
[16] BUSARI M, WILLIAMSON C. ProWGen:a synthetic workload generation tool for simulation evaluation of Web proxy caches[J]. Computer Networks, 2002, 38(6):779-794.
[17] THOMPSON J R, WILSON J R. Multifractal detrended fluctuation analysis:practical applications to financial time series[J]. Mathematics and Computers in Simulation, 2016, 126(C):63-88.

基于稳态过程的多重分形Web日志仿真生成算法

Multi-fractal Web log simulation generation algorithm based on stable process

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	李洋莹, 陈智军, 张子豪, 游兰. 基于改进Elman神经网络的制糖企业原糖需求预测模型[J]. 计算机应用, 2021, 41(7): 2113-2120.
[2]	姚杰, 程春玲, 韩静, 刘峥. 云工作流中基于多任务时序卷积网络的异常检测方法[J]. 计算机应用, 2021, 41(6): 1701-1708.
[3]	李国荣, 冶继民, 甄远婷. 基于新的鲁棒相似性度量的时间序列聚类[J]. 计算机应用, 2021, 41(5): 1343-1347.
[4]	曹阳, 闫秋艳, 吴鑫. 不平衡时间序列集成分类算法[J]. 计算机应用, 2021, 41(3): 651-656.
[5]	张凌哲, 黄向东, 乔嘉林, 勾王敏浩, 王建民. 面向时序数据的两阶段日志结构合并树文件合并框架[J]. 计算机应用, 2021, 41(3): 618-622.
[6]	马停停, 冀天娇, 杨冠羽, 陈阳, 许文波, 刘宏图. 基于长短时记忆神经网络的手足口病发病趋势预测[J]. 计算机应用, 2021, 41(1): 265-269.
[7]	钱斌, 郑楷洪, 陈子鹏, 肖勇, 李森, 叶纯壮, 马千里. 基于残差连接长短期记忆网络的时间序列修复模型[J]. 计算机应用, 2021, 41(1): 243-248.
[8]	肖勇, 郑楷洪, 郑镇境, 钱斌, 李森, 马千里. 基于多尺度跳跃深度长短期记忆网络的短期多变量负荷预测[J]. 计算机应用, 2021, 41(1): 231-236.
[9]	高世乐, 王滢, 李海林, 万校基. 基于矩阵画像的金融时序数据预测方法[J]. 计算机应用, 2021, 41(1): 199-207.
[10]	苟子安, 张晓, 吴东南, 王艳秋. 分布式存储系统中的日志分析与负载特征提取[J]. 计算机应用, 2020, 40(9): 2586-2593.
[11]	郭秀婷, 朱昶胜, 张生财, 赵奎鹏. 分形插值在风速时间序列中的应用[J]. 计算机应用, 2020, 40(9): 2628-2633.
[12]	胡珉, 白雪, 徐伟, 吴秉键. 多维时间序列异常检测算法综述[J]. 计算机应用, 2020, 40(6): 1553-1564.
[13]	霍纬纲, 王慧芳. 基于自编码器和隐马尔可夫模型的时间序列异常检测方法[J]. 计算机应用, 2020, 40(5): 1329-1334.
[14]	张丽, 孔旭, 孙忠贵. 基于非局部自相似性和低秩矩阵逼近的补全算法[J]. 计算机应用, 2020, 40(11): 3327-3331.
[15]	姜逸凡, 叶青. 基于孪生神经网络的时间序列相似性度量[J]. 计算机应用, 2019, 39(4): 1041-1045.