hLSTM-Aging: A Hybrid LSTM Model for Software Aging Forecast
Source abstract
Abstract
Long-running software, such as cloud computing services, is now widely used in modern applications. As a result, the demand for high availability and performance has grown. However, these applications are more vulnerable to software aging issues and are more likely to fail due to the accumulation of mistakes in the system. One popular strategy for dealing with such aging-related problems is to plan prediction-based software rejuvenation activities based on previously obtained data from long-running software. Prediction algorithms enable the activation of a mitigation mechanism before the problem occurs. The long short-term memory (LSTM) neural network, the present state of the art in temporal series prediction, has demonstrated promising results when applied to software aging concerns. This study aims to anticipate software aging failures using a hybrid prediction model integrating long short-term memory models and statistical approaches. We emphasize the capabilities of each strategy in various long-running software scenarios and provide an untried hybrid model (hLSTM-aging) based on the union of Conv-LSTM networks and probabilistic methodologies, attempting to combine the strengths of both approaches for software aging forecasts. The hLSTM-aging prediction results revealed how hybrid models are a compelling solution for software-aging prediction. Experiments showed that hLSTM-aging increased MSE criteria by 8.54% to 50% and MAE criteria by 3.53% to 14.29% when compared to Conv-LSTM, boosting the model’s initial performance.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Traffic Prediction and Management Techniques · Data Quality and Management · Software System Performance and Reliability
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Departamento de Informática, Universidade Federal do Agreste de Pernambuco, Garanhuns 55292-270, Brazil
School of Software, College of Computer Science, Kookmin University, Seoul 02707, Korea
Konkuk Aerospace Design-Airworthiness Research Institute (KADA), Konkuk University, Seoul 05029, Korea
Department of Computer Science and Engineering, College of Engineering, Konkuk University, Seoul 05029, Korea
Funding
Ministry of Education
References (32)
Show source-provided references
1. Grottke, M., Matias, R., and Trivedi, K. (2008, January 11–14). The fundamentals of software aging. Proceedings of the IEEE International Conference on Software Reliability Engineering Workshops, Seattle, WA, USA.10.1109/ISSREW.2008.5355512 ↗
2. A Survey of Software Aging and Rejuvenation Studies10.1145/2539117 ↗
3. Alonso, J., Belanche, L., and Avresky, D.R. (2011, January 25–27). Predicting software anomalies using machine learning techniques. Proceedings of the IEEE 10th International Symposium on Network Computing and Applications, Cambridge, MA, USA.10.1109/NCA.2011.29 ↗
4. Software Aging in the Eucalyptus Cloud Computing Infrastructure: Characterization and Rejuvenation10.1145/2539122 ↗
5. Sudhakar, C., Shah, I., and Ramesh, T. (2014, January 11–13). Software rejuvenation in cloud systems using neural networks. Proceedings of the International Conference on Parallel, Distributed and Grid Computing, Solan, India.10.1109/PDGC.2014.7030747 ↗
6. Liu, J., Tan, X., and Wang, Y. (2019, January 8–13). CSSAP: Software aging prediction for cloud services based on ARIMA-LSTM hybrid model. Proceedings of the IEEE International Conference on Web Services (ICWS), Milan, Italy.10.1109/ICWS.2019.00055 ↗
7. Recurrent neural networks and robust time series prediction10.1109/72.279188 ↗
8. An Application Of Time Series Arima Forecasting Model For Predicting Sugarcane Production In India
9. Permanasari, A., Hidayah, I., and Bustoni, I.A. (2013, January 7–8). SARIMA (Seasonal ARIMA) implementation on time series to forecast the number of Malaria incidence. Proceedings of the International Conference on Information Technology and Electrical Engineering (ICITEE), Yogyakarta, Indonesia.10.1109/ICITEED.2013.6676239 ↗
10. Hannan, E.J. (2009). Multiple Time Series, John Wiley & Sons.
11. Interpretability application of the Just-in-Time software defect prediction model10.1016/j.jss.2022.111245 ↗
12. An empirical study to investigate oversampling methods for improving software defect prediction using imbalanced data10.1016/j.neucom.2018.04.090 ↗
13. Araujo, J., Matos, R., Maciel, P., Vieira, F., Matias, R., and Trivedi, K.S. (December, January 29). Software rejuvenation in eucalyptus cloud computing infrastructure: A method based on time series forecasting and multiple thresholds. Proceedings of the IEEE Third International Workshop on Software Aging and Rejuvenation, Hiroshima, Japan.
14. Avresky, D.R., Pellegrini, A., and Di Sanzo, P. (November, January 30). Machine learning-based management of cloud applications in hybrid clouds: A Hadoop case study. Proceedings of the IEEE 16th International Symposium on Network Computing and Applications (NCA), Cambridge, MA, USA.
15. Qiao, Y., Zheng, Z., and Fang, Y. (2018, January 15–18). An empirical study on software aging indicators prediction in android mobile. Proceedings of the IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW), Memphis, TN, USA.10.1109/ISSREW.2018.00018 ↗
16. Prediction of early stabilization time of electrolytic capacitor based on ARIMA-Bi_LSTM hybrid model10.1016/j.neucom.2020.03.054 ↗
17. Assessing CO2 emissions in China’s iron and steel industry: A dynamic vector autoregression model10.1016/j.apenergy.2015.10.039 ↗
18. Time Series Forecasting Using a Moving Average Model for Extrapolation of Number of Tourist
19. Electricity Demand Loads Modeling using AutoRegressive Moving Average (ARMA) models10.1016/j.energy.2008.05.008 ↗
20. A Novel Method for SoH Prediction of Batteries Based on Stacked LSTM with Quick Charge Data10.1080/08839514.2021.1901033 ↗
21. Rahman, M., Watanobe, Y., and Nakamura, K. (2021). A Bidirectional LSTM Language Model for Code Evaluation and Repair. Symmetry, 13.10.3390/sym13020247 ↗
22. He, Z., Zhou, J., Dai, H.N., and Wang, H. (2019, January 5–8). Gold Price Forecast Based on LSTM-CNN Model. Proceedings of the IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), Fukuoka, Japan.10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00188 ↗
23. Huang, Y., Kintala, C., Kolettis, N., and Fulton, N. (1995, January 27–30). Software rejuvenation: Analysis, module and applications. Proceedings of the Twenty-Fifth International Symposium on Fault-Tolerant Computing, Pasadena, CA, USA.
24. Laird, L. (2006). Software Measurement and Estimation: A Practical Approach, John Wiley & Sons.10.1002/0471792535 ↗
25. Matias, R., and Filho, P.J.F. (2006, January 17–21). An experimental study on software aging and rejuvenation in web servers. In Proceedings of the 30th Annual International Computer Software and Applications Conference (COMPSAC’06), Chicago, IL, USA.10.1109/COMPSAC.2006.25 ↗
26. Chen, C.W.S., and Chiu, L.M. (2021). Ordinal Time Series Forecasting of the Air Quality Index. Entropy, 23.10.3390/e23091167 ↗
27. Brockwell, P.J., and Davis, R.A. (1991). Stationary time series. Time Series: Theory and Methods, Springer.10.1007/978-1-4419-0320-4 ↗
28. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data10.1016/j.trc.2015.03.014 ↗
29. Shumway, R.H., and Stoffer, D.S. (2005). Time Series Analysis and Its Applications (Springer Texts in Statistics), Springer.
30. Hyndman, R., and Athanasopoulos, G. (2018). Forecasting: Principles and Practice, OTexts. [2nd ed.].
31. Oliveira, F., Araujo, J., Matos, R., Lins, L., Rodrigues, A., and Maciel, P. (2020, January 11–14). Experimental Evaluation of Software Aging Effects in a Container-Based Virtualization Platform. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, ON, Canada.10.1109/SMC42975.2020.9283358 ↗
32. Estimating the Dimension of a Model10.1214/aos/1176344136 ↗