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Time series forecast modeling for the Windows operating system performance using Box-Jenkins and LSTM models | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 170، دوره 13، شماره 2، مهر 2022، صفحه 2121-2131 اصل مقاله (1.12 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.26974.3505 | ||
| نویسندگان | ||
| Murtadha Jabbar Assi* ؛ Assmaa A. Fahad؛ Basad Al-Sarray | ||
| Department of Computer science, University of Baghdad, Baghdad, Iraq | ||
| تاریخ دریافت: 19 اردیبهشت 1401، تاریخ بازنگری: 16 تیر 1401، تاریخ پذیرش: 24 تیر 1401 | ||
| چکیده | ||
| Performance issues such as system resources leaking, application hang, and Software Aging (SA) can affect the system's reliability and minimize user experiences. Therefore, these issues need to be analyzed and forecasted to prevent incoming issues. Finding the root cause and analyzing the internal behaviors become troublesome due to the complexity of modern systems such as the Microsoft Windows Operating System OS. Microsoft builds multiple tools and platforms such as the Performance Monitor (PerfMon.exe) tool and Performance Counter for Windows (PCW) platform to monitor the activities inside Windows OS. This paper aims to use Windows OS tools for simulating performance issues in an experiment, data collection, and log format converting. In contrast to other works, the deep learning Long Short-Term Memory (LSTM) method and the Auto-regressive Integrated Moving Average (ARIMA) model were generated and compared. The best model that provides the lowest error rate of the prediction simulated performance issue was selected. The results declare the preference of using the ARIMA model with order (2,1,1) that provides the observed lowest error rate for both MAE and RMSE compared with other values in previous lags. And the observed LSTM has an error rate of 4.796, whereas the ARIMA model has an error rate of 0.0119. From those results, we can confirm of using the ARIMA model with its selected parameters can predict the small jump fluctuations behavior observed from the memory metric. | ||
| کلیدواژهها | ||
| Log؛ Memory leak؛ Perfmon؛ Forecast؛ Time series analysis | ||
| مراجع | ||
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[1] A. Agelastos, B. Allan, J.Brandt, P. Cassella, J. Enos, J. Fullop, A. Gentile, S. Monk, N. Naksinehaboon, J. Ogden, M. Rajan, M. Showerman, J. Stevenson, N. Taerat and T. Tucker, The lightweight distributed metric service: A scalable infrastructure for continuous monitoring of large scale computing systems and applications, Int. Conf. High Perform. Comput. Networking, Storage Anal. SC 2015, pp. 154–165. [2] S.H.J. Alsaedi, Forecasting the numbers of cardiac diseases patients by using Box-Jenkins model in time series analysis, Int. J. Nonlinear Anal. Appl. 13 (2022), no. 1, 1673–1681.[3] N.H. Albin Zehe, A.H. Andr´e Bauer, M. Zufle and S. Kounev, Time series forecasting for self-aware system, Schloss Dagstuhl 5 (2020). [4] B. Auffarth, Machine Learning for Time-Series with Python, Packt, 2021. [5] C. Bezemer, E. Milon, A. Zaidman and J. Pouwelse, Detecting and analyzing I/O performance regressions, J. Softw. Evol. Process 26 (2014), no. 12, 1193–1212. [6] G.E.P. Box, G.M. Jenkins, G.C. Reinsel and G. M. Ljung, Time series analysis: forecasting and control, John Wiley & Sons, 2015. [7] P.P. Deb and I. Chatterjee, Time-series forecasting using lstm, Schloss Dagstuhl 5 (2022). [8] D.A. Dickey and W.A. Fuller, Distribution of the Estimators for Autoregressive Time Series With a Unit Root, J. Amer. Statist. Assoc. 74 (1979), no. 366, 427–431 (1986). [9] M. Ficco, R.Pietrantuono and S. Russo, Aging-related performance anomalies in the apache storm stream processing system, Futur. Gener. Comput. Syst. 86 (2018), 975–994. [10] K. Gencer and F. Ba¸s¸cift¸ci, Time series forecast modeling of vulnerabilities in the android operating system using ARIMA and deep learning methods, Sustain. Comput. Informatics Syst. 30 (2021). [11] M. Grottke, R. Matias, and K.S. Trivedi, The fundamentals of software aging, IEEE Int. Conf. Softw. Reliab. Eng. Work. ISSRE Wksp, 2008. [12] Y. Huang, C. Kintala, N. Kolettis and N. Dudley Fulton, Software rejuvenation: Analysis, module and applications, Proc. Annu. Int. Conf. Fault-Tolerant Comput., 1995, pp. 381–390. [13] C. Huffman, Windows Performance Analysis Field Guide, Elsevier, 2014. [14] M. Kubacki and J. Sosnowski, Exploring operational profiles and anomalies in computer performance logs, Microproc. Microsyst. 69 (2019), 1–15. [15] Guineng Zheng Vivek Srikumar Min Du, F. Li, Deeplog: Anomaly detection and diagnosis from system logs through deep learning, Proc. 2017 ACM SIGSAC Conf.Comput. Commun. Security, 2017. no. Oct 2017. [16] I.M. Umesh, G.N. Srinivasan and M.Torquato, Software aging forecasting using time series model, Indones. J. Electr. Eng. Comput. Sci. 8 (2017), no. 3, 589–596. | ||
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