Cloud Node Auto-Scaling System Automation Based on Computing Workload Prediction
Abstract
Auto-scaling systems in cloud computing are important for handling application workload fluctuations. This research uses machine learning to predict resource requirements based on workload work patterns and design an automatic scaling system. The dataset used includes features of node name, time, CPU usage percentage, and RAM usage. The ML model is applied for prediction regression of CPU usage percentage, CPU load, and RAM usage, and then server workload is classified into four categories: Very High, High, Low, and Very Low. The autoscaling system used is horizontal scaling. From the results of this research, it was found that the stacking algorithm with the base learner Random Forest and XGBoost had better performance in producing predictive regression. Then, after performing stability testing using K-Fold cross-validation by classifying based on workload status, it was found that the Gradient Boosting algorithm had better results compared to other algorithms, namely for the percentage of CPU usage with an accuracy of 0.998, precision 0.9, recall 0.878, f1score 0.888; CPU load average 15 minutes with accuracy 0.997, precision 0.854, recall 0.863, f1score 0.863; Meanwhile, the percentage of RAM usage is accuracy 0.992, precision 0.986, recall 0.986, and f1score 0.986. However, the XGBoost algorithm also has test results that are almost the same as Gradient Boosting.
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A. S. Balantimuhe, S. H. Pramono, and H. Suyono, “Konsolidasi Beban Kerja Kluster Web Server Dinamis dengan Pendekatan Backpropagation Neural Network,” Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems), vol. 12, no. 2, pp. 72–77, Sep. 2018, doi: 10.21776/jeeccis.v12i2.536.
A. M. Al-Faifi, B. Song, M. M. Hassan, A. Alamri, and A. Gumaei, “Performance prediction model for cloud service selection from smart data,” Future Generation Computer Systems, vol. 85, pp. 97–106, Aug. 2018, doi: 10.1016/j.future.2018.03.015.
P. D. Adane and O. G. Kakde, “Predicting Resource Utilization for Cloud Workloads Using Machine Learning Techniques,” in Proceedings of the 2nd International Conference on Inventive Communication and Computational Technologies (ICICCT), 2018, pp. 1372–1376.
A. A. Khaleq and I. Ra, “Intelligent Autoscaling of Microservices in the Cloud for Real-Time Applications,” IEEE Access, vol. 9, pp. 35464–35476, 2021, doi: 10.1109/ACCESS.2021.3061890.
S. T. Singh, M. Tiwari, and A. S. Dhar, “Machine Learning based Workload Prediction for Auto-scaling Cloud Applications,” in 2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022, Institute of Electrical and Electronics Engineers Inc., 2023. doi: 10.1109/OTCON56053.2023.10114033.
W. Iqbal, A. Erradi, M. Abdullah, and A. Mahmood, “Predictive Auto-Scaling of Multi-Tier Applications Using Performance Varying Cloud Resources,” IEEE Transactions on Cloud Computing, vol. 10, no. 1, pp. 595–607, 2022, doi: 10.1109/TCC.2019.2944364.
S. Manam, K. Moessner, and P. Asuquo, “A Machine Learning Approach to Resource Management in Cloud Computing Environments,” in IEEE AFRICON Conference, Institute of Electrical and Electronics Engineers Inc., 2023. doi: 10.1109/AFRICON55910.2023.10293275.
R. A. ) Eric Bauer, Reliability and Availability of Cloud Computing. Wiley-IEEE Press, 2012.
V. Millnert and J. Eker, “HoloScale: horizontal and vertical scaling of cloud resources,” in 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC), 2020, pp. 196–205. doi: 10.1109/UCC48980.2020.00038.
C.-Y. Liu, M.-R. Shie, Lee Yi-Fang, and K.-C. Lai, ICISA 2014 : 2014 Fifth International Conference on Information Science and Applications : 6-9 May, 2014, Seoul, Korea. 2014.
J. Bi et al., “Application-Aware Dynamic Fine-Grained Resource Provisioning in a Virtualized Cloud Data Center,” IEEE Transactions on Automation Science and Engineering, vol. 14, no. 2, pp. 1172–1184, 2017, doi: 10.1109/TASE.2015.2503325.
V. P. M. Arif Wani, Deep Learning Applications, Volume 4. in Advances in Intelligent Systems and Computing, 1434. Springer, 2023.
T. G. Dietterich, “Ensemble Methods in Machine Learning,” in Multiple Classifier Systems, Berlin, Heidelberg: Springer Berlin Heidelberg, 2000, pp. 1–15.
I. D. Mienye and Y. Sun, “A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects,” IEEE Access, vol. 10, pp. 99129–99149, 2022, doi: 10.1109/ACCESS.2022.3207287.
C. Zhao, R. Peng, and D. Wu, “Bagging and Boosting Fine-Tuning for Ensemble Learning,” IEEE Transactions on Artificial Intelligence, vol. 5, no. 4, pp. 1728–1742, 2024, doi: 10.1109/TAI.2023.3296685.
L. Breiman, “Random Forests,” Mach Learn, vol. 45, no. 1, pp. 5–32, Oct. 2001, doi: 10.1023/A:1010933404324.
J. H. Friedman, “Greedy function approximation: A gradient boosting machine.,” The Annals of Statistics, vol. 29, no. 5, pp. 1189 – 1232, 2001, doi: 10.1214/aos/1013203451.
T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, in KDD ’16. New York, NY, USA: Association for Computing Machinery, 2016, pp. 785–794. doi: 10.1145/2939672.2939785.
D. H. Wolpert, “Stacked generalization,” Neural Networks, vol. 5, no. 2, pp. 241–259, 1992, doi: https://doi.org/10.1016/S0893-6080(05)80023-1.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016.
S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput, vol. 9, no. 8, pp. 1735–1780, Jul. 1997, doi: 10.1162/neco.1997.9.8.1735.
S. Sharma, R. Garg, and D. K. Lobiyal, “Load Balancing Algorithms in Cloud Computing: A Comparative Study,” International Journal of Advanced Research in Computer Science and Software Engineering, 2014.
S. Muthukrishnan and V. Sankaranarayanan, “A Survey of Load Balancing Techniques in Cloud Computing Environments,” Journal of Network and Computer Applications, 2016.
M. G. Nair, S. Bhuvaneswari, and S. S. Baboo, “A Survey of Load Balancing in Cloud Computing: Challenges and Algorithms,” Int J Comput Appl, 2015.
T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2009.
G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning: with Applications in R. Springer, 2013.
H. T. Jiawei Han Jian Pei, Data Mining: Concepts and Techniques, 4th ed. in The Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann, 2022.
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