乘积季节模型在软件老化评估中的应用研究
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摘要
在需要长期运行的系统中,软件老化是一种常见的现象,现有基于时序分析的软件老化评估方法,大多基于简单的自回归或ARMA模型,没有充分考虑软件老化关键指标的非平稳性、季节性等特征。该文提出一种基于乘积季节ARIMA模型的软件老化评估方法。并通过实验表明,该方法能够较好地拟合季节性负载系统的软件老化趋势,并能做出准确的预测以支撑软件再生。
Software aging is a common phenomenon in a system that needs long-term operation. The existing analysis methods based on time series analysis mainly focus on autoregressive moving average(ARMA) models, not fully considered the seasonality or non-stationarity of the key indicators about software aging. This paper proposes a new software aging evaluation method based on seasonal autoregressive integrated moving average(ARIMA) model. The experimental results show that the method can well fit the software aging trend of seasonal load systems, and can achieve accurate prediction for supporting software rejuvenation.
Software aging is a common phenomenon in a system that needs long-term operation. The existing analysis methods based on time series analysis mainly focus on autoregressive moving average(ARMA) models, not fully considered the seasonality or non-stationarity of the key indicators about software aging. This paper proposes a new software aging evaluation method based on seasonal autoregressive integrated moving average(ARIMA) model. The experimental results show that the method can well fit the software aging trend of seasonal load systems, and can achieve accurate prediction for supporting software rejuvenation.
引文
[1]HUANG Y,KINTALA C,KOLETTIS N,et al.Software rejuvenation:Analysis,module and applications[C]//Twenty-Fifth International Symposium on Fault-Tolerant Computing,1995,FTCS-25.[S.l.]:IEEE,1995:381-390.
[2]MACHIDA F,XIANG J,TADANO K,et al.Combined server rejuvenation in a virtualized data center[C]//2012 9th International Conference on Ubiquitous Intelligence&Computing and 9th International Conference on Autonomic&Trusted Computing(UIC/ATC).[S.l.]:IEEE,2012:486-493.
[3]DOMENICO C,ROBERTO N,ROBERTO P,et al.Software aging analysis of the linux operating system[C]//In Proc of Int'l Symp on Software Reliability Engineering(ISSRE2010).[S.l.]:IEEE,2010:71-80.
[4]ARAUJO J,MATOS R,MACIEL P,et al.Experimental evaluation of software aging effects on the eucalyptus cloud computing infrastructure[C]//Proceedings of the Middleware2011 Industry Track Workshop.[S.l.]:ACM,2011:4.
[5]COTRONEO D,ORLANDO S,RUSSO S.Characterizing aging phenomena of the java virtual machine[C]//26th IEEE International Symposium on Reliable Distributed Systems,2007,SRDS 2007.[S.l.]:IEEE,2007:127-136.
[6]GROTTKE M,LI L,VAIDYANATHAN K,et al.Analysis of software aging in a web server[J].IEEE Transactions on Reliability,2006,55(3):411-420.
[7]LI L,VAIDYANATHAN K,TRIVEDI K S.An approach for estimation of software aging in a web server[C]//Empirical Software Engineering,International Symposium.[S.l.]:IEEE,2002:91-100.
[8]杜小智,齐勇,鲁慧民,等.视频点播系统的软件老化估计和预测[J].计算机研究与发展,2012,48(11):2139-2146.DU Xiao-zhi,QI Yong,LU Hui-min,et al.Software aging pattern analysis of the video on demand system[J].Journal of Computer Research and Development,2011,48(11):2139-2146.
[9]郑鹏飞,齐勇,陈鹏飞.软件老化的多元时间序列分析方法[J].计算机科学与探索,2012,6(2):125-133.ZHENG Peng-fei,QI Yong,CHEN Peng-fei.Multivariate time series analysis of software aging[J].Journal of Frontiers of Computer Science and Technology,2012,6(2):125-133.
[10]WILLIAMS A,ARLITT M,WILLIAMSON C,et al.Web workload characterization:Ten years later[M]//Web Content Delivery.[S.l.]:Springer US,2005:3-21.
[11]ALONSO J,TORRES J,BERRAL J,et al.Adaptive on-line software aging prediction based on machine learning[C]//Proceedings of the IEEE/IFIP International Conference on Dependable Systems and Networks.Washington,DC,USA:IEEE Computer Society,2010:507-516.
[12]CHATFIELD C.The analysis of time series:an introduction[M].[S.l.]:CRC,2013.
[13]巩师恩,范从来.二元劳动力结构与通货膨胀动态形成机制——基于新凯恩斯菲利普斯曲线框架[J].财经研究,2013,3:75-86.GONG Shi-en,FAN Cong-lai.Dual labor structure and dynamic formation mechanism of inflation:Based on the framework of new keynesian phillips curve[J].Journal of Finance and Economics,2013,3:75-86.
[14]CHEN K Y,WANG C H.A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan[J].Expert Systems with Applications,2007,32(1):254-264.
[15]TSENG F M,TZENG G H.A fuzzy seasonal ARIMA model for forecasting[J].Fuzzy Sets and Systems,2002,126(3):367-376.
[16]袁小坊,陈楠楠,王东,等.城域网应用层流量预测模型[J].计算机研究与发展,2009,46(3):434-442.YUAN Xiao-fang,CHEN Nan-nan,WANG Dong,et al.Traffic prediction models of traffics at application layer in metro area network[J].Journal of Computer Research and Development,2009,46(3):434-442.
[17]黄建国,罗航,王厚军,等.运用GA-BP神经网络研究时间序列的预测[J].电子科技大学学报,2009,38(5):687-692.HUANG Jian-guo,LUO Hang,WANG Hou-jun,et al.Prediction of time sequence based on GA-BP neural net[J].Journal of University of Electronic Science and Technology of China,2009,38(5):687-692.
[18]吴少智,吴跃,徐鹏,等.支持向量回归的颅内压时间系列无损估计方法[J].电子科技大学学报,2011,40(6):956-960.WU Shao-zhi,WU Yue,XU Peng,et al.Support vector regression based time series mining approach for non-invasive ICP assessment[J].Journal of University of Electronic Science and Technology of China,2011,40(6):956-960.
[19]R-Project.Rcasting fnctions for tme sries and lnear mdels[EB/OL].[2015-11-01].http://cran.r-project.org/web/packages/forecast/index.html.
[2]MACHIDA F,XIANG J,TADANO K,et al.Combined server rejuvenation in a virtualized data center[C]//2012 9th International Conference on Ubiquitous Intelligence&Computing and 9th International Conference on Autonomic&Trusted Computing(UIC/ATC).[S.l.]:IEEE,2012:486-493.
[3]DOMENICO C,ROBERTO N,ROBERTO P,et al.Software aging analysis of the linux operating system[C]//In Proc of Int'l Symp on Software Reliability Engineering(ISSRE2010).[S.l.]:IEEE,2010:71-80.
[4]ARAUJO J,MATOS R,MACIEL P,et al.Experimental evaluation of software aging effects on the eucalyptus cloud computing infrastructure[C]//Proceedings of the Middleware2011 Industry Track Workshop.[S.l.]:ACM,2011:4.
[5]COTRONEO D,ORLANDO S,RUSSO S.Characterizing aging phenomena of the java virtual machine[C]//26th IEEE International Symposium on Reliable Distributed Systems,2007,SRDS 2007.[S.l.]:IEEE,2007:127-136.
[6]GROTTKE M,LI L,VAIDYANATHAN K,et al.Analysis of software aging in a web server[J].IEEE Transactions on Reliability,2006,55(3):411-420.
[7]LI L,VAIDYANATHAN K,TRIVEDI K S.An approach for estimation of software aging in a web server[C]//Empirical Software Engineering,International Symposium.[S.l.]:IEEE,2002:91-100.
[8]杜小智,齐勇,鲁慧民,等.视频点播系统的软件老化估计和预测[J].计算机研究与发展,2012,48(11):2139-2146.DU Xiao-zhi,QI Yong,LU Hui-min,et al.Software aging pattern analysis of the video on demand system[J].Journal of Computer Research and Development,2011,48(11):2139-2146.
[9]郑鹏飞,齐勇,陈鹏飞.软件老化的多元时间序列分析方法[J].计算机科学与探索,2012,6(2):125-133.ZHENG Peng-fei,QI Yong,CHEN Peng-fei.Multivariate time series analysis of software aging[J].Journal of Frontiers of Computer Science and Technology,2012,6(2):125-133.
[10]WILLIAMS A,ARLITT M,WILLIAMSON C,et al.Web workload characterization:Ten years later[M]//Web Content Delivery.[S.l.]:Springer US,2005:3-21.
[11]ALONSO J,TORRES J,BERRAL J,et al.Adaptive on-line software aging prediction based on machine learning[C]//Proceedings of the IEEE/IFIP International Conference on Dependable Systems and Networks.Washington,DC,USA:IEEE Computer Society,2010:507-516.
[12]CHATFIELD C.The analysis of time series:an introduction[M].[S.l.]:CRC,2013.
[13]巩师恩,范从来.二元劳动力结构与通货膨胀动态形成机制——基于新凯恩斯菲利普斯曲线框架[J].财经研究,2013,3:75-86.GONG Shi-en,FAN Cong-lai.Dual labor structure and dynamic formation mechanism of inflation:Based on the framework of new keynesian phillips curve[J].Journal of Finance and Economics,2013,3:75-86.
[14]CHEN K Y,WANG C H.A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan[J].Expert Systems with Applications,2007,32(1):254-264.
[15]TSENG F M,TZENG G H.A fuzzy seasonal ARIMA model for forecasting[J].Fuzzy Sets and Systems,2002,126(3):367-376.
[16]袁小坊,陈楠楠,王东,等.城域网应用层流量预测模型[J].计算机研究与发展,2009,46(3):434-442.YUAN Xiao-fang,CHEN Nan-nan,WANG Dong,et al.Traffic prediction models of traffics at application layer in metro area network[J].Journal of Computer Research and Development,2009,46(3):434-442.
[17]黄建国,罗航,王厚军,等.运用GA-BP神经网络研究时间序列的预测[J].电子科技大学学报,2009,38(5):687-692.HUANG Jian-guo,LUO Hang,WANG Hou-jun,et al.Prediction of time sequence based on GA-BP neural net[J].Journal of University of Electronic Science and Technology of China,2009,38(5):687-692.
[18]吴少智,吴跃,徐鹏,等.支持向量回归的颅内压时间系列无损估计方法[J].电子科技大学学报,2011,40(6):956-960.WU Shao-zhi,WU Yue,XU Peng,et al.Support vector regression based time series mining approach for non-invasive ICP assessment[J].Journal of University of Electronic Science and Technology of China,2011,40(6):956-960.
[19]R-Project.Rcasting fnctions for tme sries and lnear mdels[EB/OL].[2015-11-01].http://cran.r-project.org/web/packages/forecast/index.html.