Prediksi Beban Kerja Server Secara Real-Time pada Pusat Data Cloud dengan Pendekatan Gabungan Long Short-Term Memory (LSTM) dan Fuzzy Logic
DOI:
https://doi.org/10.35746/jtim.v7i3.731Keywords:
Cloud Data Center, fuzzy logic, LSTM, workload prediction, Energy EfficiencyAbstract
Efficient resource management in Cloud Data Centers is essential to reduce energy waste and maintain optimal system performance. This study aims to predict server workload in real time using a hybrid approach that combines Long Short-Term Memory (LSTM) and Fuzzy Logic. CPU and RAM usage data were collected every second from a Proxmox Cluster using its API, then normalized and processed using an LSTM model to forecast future workloads. The predicted results were then classified using Fuzzy Logic into three workload categories: light, medium, and heavy. The model was evaluated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), where the results showed an MAE of 2.48 on the training data and 3.09 on the testing data, as well as RMSE values of 5.15 and 5.57, respectively. Based on these evaluation results, the prediction system achieved an accuracy of 97.52% on the training data and 96.91% on the testing data, indicating that the model can generate accurate and stable predictions. This method enables automated decision-making such as workload-based power management, thereby improving energy efficiency and overall system performance.
Downloads
References
C. Guo, F. Luo, Z. Cai, and Z. Y. Dong, “Integrated energy systems of data centers and smart grids: State-of-the-art and future opportunities,” Applied Energy, vol. 301, 2021, pp. 117474, https://doi.org/10.1016/j.apenergy.2021.117474
K.-T. Seo, H.-S. Hwang, I.-Y. Moon, O.-Y. Kwon, and B.-J. Kim, “Performance comparison analysis of Linux container and virtual machine for building cloud,” Advanced Science and Technology Letters, vol. 66, pp. 105–111, 2014, https://doi.org/10.14257/astl.2014.66.25.
A. Alarifi, et al., “Energy-efficient hybrid framework for green cloud computing,” IEEE Access, vol. 8, pp. 115356–115369, 2020, https://doi.org/10.1109/ACCESS.2020.3002184
M. Ibrahim, M. Imran, F. Jamil, Y. J. Lee, and D. H. Kim, “EAMA: Efficient adaptive migration algorithm for cloud data centers (CDCs),” Symmetry, vol. 13, no. 4, 2021, https://doi.org/10.3390/sym13040690
Y. Xie *et al*., “Real-time prediction of Docker container resource load based on a hybrid model of ARIMA and triple exponential smoothing,” IEEE Trans. Cloud Comput, vol. 10, no. 2, pp. 1386–1401, 2022, https://doi.org/10.1109/TCC.2020.2989631
R. Shi and C. Jiang, “Three-way ensemble prediction for workload in the data center,” IEEE Access, vol. 10, pp. 10021–10030, 2022, https://doi.org/10.1109/ACCESS.2022.3145426
S. ?tefan and V. Niculescu, “Microservice-oriented workload prediction using deep learning,” E-Inform. Softw. Eng. J., vol. 16, no. 1, 2022, https://doi.org/10.37190/e-Inf220107
B. Gülmez, “Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algo-rithm,” Expert Syst. Appl., vol. 227, Art. no. 120346, 2023, https://doi.org/10.1016/j.eswa.2023.120346
Y. Lin, Y. Yan, J. Xu, Y. Liao, and F. Ma, “Forecasting stock index price using the CEEMDAN-LSTM model,” N. Am. J. Econ. Finance, vol. 57, 2021, Art. no. 101421, https://doi.org/10.1016/j.najef.2021.101421
M. P. Yadav, N. Pal, and D. K. Yadav, “Workload prediction over cloud server using time series data,” in Proc. Confluence 2021: 11th Int. Conf. Cloud Comput., Data Sci. Eng., pp. 267–272, 2021, https://doi.org/10.1109/Confluence51648.2021.9377032
J.-S. R. Jang, “ANFIS: Adaptive-network-based fuzzy inference system,” IEEE Trans. Syst., Man, Cybern., vol. 23, no. 3, 1993, https://doi.org/10.1109/21.256541
Z. Amekraz and M. Y. Hadi, “A cluster workload forecasting strategy using a higher order statistics based ARMA model for IaaS cloud services,” Int. J. Netw. Virtual Organ., vol. 26, no. 1/2, p. 3, 2022, https://doi.org/10.1504/IJNVO.2022.121844
M. Koot and F. Wijnhoven, “Usage impact on data center electricity needs: A system dynamic forecasting model,” Appl. Energy, vol. 291, 2021, Art. no. 116798, https://doi.org/10.1016/j.apenergy.2021.116798
Z. Yang and H. Cao, “Preprocessing is not needed: An end-to-end solution for physiological signals based emotion recognition,” 2024, https://openreview.net/pdf?id=egwH8GLlMN
T. O. Hodson, “Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not,” Geosci. Model Dev., vol. 15, no. 14, pp. 5481–5487, 2022, https://doi.org/10.5194/gmd-15-5481-2022
A. Salamh, M. Al-Rawahnaa, A. Y. Bader, and A. Hadid, “Data mining for education sector, a proposed concept,” J. Appl. Data Sci., vol. 1, no. 1, pp. 1–10, 2020. https://bright-journal.org/Journal/index.php/JADS/article/view/6/6
Hendri, T. Wahyuningsih, and E. Rahwanto, “Comparison of Min-Max normalization and Z-score normalization in the K-nearest neighbor (KNN) algorithm to test the accuracy of types of breast cancer,” UCI Machine Learning Re-pository, 2021, https://ijiis.org/index.php/IJIIS/article/view/73
N. Sulistianingsih and G. H. Martono, “Comparative study on stock movement prediction using hybrid deep learning model,” ECTI Trans. Comput. Inf. Technol., vol. 18, no. 4, pp. 531–542, 2024, https://doi.org/10.37936/ecti-cit.2024184.256303
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Naufal Hanif, Dadang Priyanto, Neny Sulistianingsih
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
