The Use of Machine Learning for Efficient Energy Management in Big Data-Based Computing Systems
DOI:
https://doi.org/10.51903/jtie.v3i3.202Kata Kunci:
Machine Learning, Energy Efficiency, Data Centers, Predictive AnalyticsAbstrak
The rapid growth of digital services has intensified the energy demands of data centers, significantly impacting operational costs and global carbon footprints. This study leverages Machine Learning (ML) and Big Data to optimize energy management in data centers, addressing limitations in prior approaches that overlooked real-time variability. A predictive model utilizing the Random Forest algorithm was developed to reduce energy consumption based on dynamic factors, such as workload and environmental conditions like temperature. The research used a six-month dataset consisting of approximately 3 million data points from an operational data center. After preprocessing the data, the model achieved a high predictive accuracy, reflected by an R-squared value of 0.87. The findings demonstrate that the model reduces energy consumption by an average of 11.17% daily, with peak savings of up to 15.56% during off-peak hours. Key metrics, including a Mean Squared Error (MSE) of 0.034 and a Root Mean Squared Error (RMSE) of 0.184, validated the model's effectiveness and reliability. Statistical tests further confirmed its precision within a 95% confidence interval. This study contributes to the academic field by integrating real-time environmental data into predictive modeling, offering a scalable solution for energy-efficient data center operations. The outcomes support sustainability initiatives by mitigating carbon emissions and reducing operational costs. The findings also provide a framework for applying ML in broader industrial contexts requiring efficient energy management. Future research may explore incorporating additional variables, such as user behavior, to further refine predictive capabilities.
Referensi
Abdel-Razek, S. A., Marie, H. S., Alshehri, A., & Elzeki, O. M. (2022). Energy Efficiency through the Implementation of an AI Model to Predict Room Occupancy Based on Thermal Comfort Parameters. Sustainability, 14(13), 7734. https://doi.org/10.3390/su14137734
Awan, U., Shamim, S., Khan, Z., Zia, N. U., Shariq, S. M., & Khan, M. N. (2021). Big Data Analytics Capability and Decision-Making: The Role of Data-Driven Insight on Circular Economy Performance. Technological Forecasting and Social Change, 168, 120766. https://doi.org/10.1016/j.techfore.2021.120766
Bharadiya, J. P. (2023). The Role of Machine Learning in Transforming Business Intelligence. International Journal of Computing and Artificial Intelligence, 4(1), 16–24. https://doi.org/10.33545/27076571.2023.v4.i1a.60
Bharany, S., Sharma, S., Khalaf, O. I., Abdulsahib, G. M., Al Humaimeedy, A. S., Aldhyani, T. H. H., Maashi, M., & Alkahtani, H. (2022). A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing. Sustainability, 14(10), 6256. https://doi.org/10.3390/su14106256
Buyya, R., Ilager, S., & Arroba, P. (2024). Energy-Efficiency and Sustainability in New Generation Cloud Computing: A Vision and Directions for Integrated Management of Data Centre Resources and Workloads. Software: Practice and Experience, 54(1), 24–38. https://doi.org/10.1002/spe.3248
Cao, Z., Zhou, X., Hu, H., Wang, Z., & Wen, Y. (2022). Toward a Systematic Survey for Carbon Neutral Data Centers. IEEE Communications Surveys and Tutorials, 24(2), 895–936. https://doi.org/10.1109/comst.2022.3161275
Fan, G. F., Zhang, L. Z., Yu, M., Hong, W. C., & Dong, S. Q. (2022). Applications of Random Forest in Multivariable Response Surface for Short-Term Load Forecasting. International Journal of Electrical Power and Energy Systems, 139, 108703. https://doi.org/10.1016/j.ijepes.2022.108073
González García, C., & Álvarez-Fernández, E. (2022). What Is (Not) Big Data Based on Its 7Vs Challenges: A Survey. Big Data and Cognitive Computing, 6(4), 158. https://doi.org/10.3390/bdcc6040158
Guo, B., Yu, J., Yang, D., Leng, H., & Liao, B. (2022). Energy-Efficient Database Systems: A Systematic Survey. ACM Computing Surveys, 55(6), 1–53. https://doi.org/10.1145/3538225
Hassani, H., & Silva, E. S. (2023). The Role of ChatGPT in Data Science: How AI-Assisted Conversational Interfaces Are Revolutionizing the Field. Big Data and Cognitive Computing, 7(2), 62. https://doi.org/10.3390/bdcc7020062
He, H., Shen, H., Hao, Q., & Tian, H. (2022). Online Delay-Guaranteed Workload Scheduling to Minimize Power Cost in Cloud Data Centers Using Renewable Energy. Journal of Parallel and Distributed Computing, 159, 51–64. https://doi.org/10.1016/j.jpdc.2021.09.002
Karthikeyan, A., & Priyakumar, U. D. (2022). Artificial Intelligence: Machine Learning for Chemical Sciences. Journal of Chemical Sciences, 134(1), 1–20. https://doi.org/10.1007/s12039-021-01995-2
Katal, A., Dahiya, S., & Choudhury, T. (2023). Energy Efficiency in Cloud Computing Data Centers: A Survey on Software Technologies. Cluster Computing, 26(3), 1845–1875. https://doi.org/10.1007/s10586-022-03713-0
Khan, T., Tian, W., Ilager, S., & Buyya, R. (2022). Workload Forecasting and Energy State Estimation in Cloud Data Centres: ML-Centric Approach. Future Generation Computer Systems, 128, 320–332. https://doi.org/10.1016/j.future.2021.10.019
Li, J., Herdem, M. S., Nathwani, J., & Wen, J. Z. (2023). Methods and applications for Artificial Intelligence, Big Data, Internet of Things, and Blockchain in smart energy management. Energy and AI, 11, 100208. https://doi.org/10.1016/j.egyai.2022.100208
Maharana, K., Mondal, S., & Nemade, B. (2022). A Review: Data Pre-Processing and Data Augmentation Techniques. Global Transitions Proceedings, 3(1), 91–99. https://doi.org/10.1016/j.gltp.2022.04.020
Munappy, A. R., Bosch, J., Olsson, H. H., Arpteg, A., & Brinne, B. (2022). Data Management for Production Quality Deep Learning Models: Challenges and Solutions. Journal of Systems and Software, 191, 111359. https://doi.org/10.1016/j.jss.2022.111359
Ouatik, F., Erritali, M., Ouatik, F., & Jourhmane, M. (2022). Predicting Student Success Using Big Data and Machine Learning Algorithms. International Journal of Emerging Technologies in Learning, 17(12), 236–251. https://doi.org/10.3991/ijet.v17i12.30259
Panwar, S. S., Rauthan, M. M. S., & Barthwal, V. (2022). A Systematic Review on Effective Energy Utilization Management Strategies in Cloud Data Centers. Journal of Cloud Computing, 11(1), 95. https://doi.org/10.1186/s13677-022-00368-5
Pfob, A., Lu, S.-C., & Sidey-Gibbons, C. (2022). Machine learning in medicine: a practical introduction to techniques for data pre-processing, hyperparameter tuning, and model comparison. BMC Medical Research Methodology, 22(1), 282. https://doi.org/10.1186/s12874-022-01758-8
Pramanik, P. K. D., Pal, S., & Mukhopadhyay, M. (2022). Healthcare Big Data. In Research Anthology on Big Data Analytics, Architectures, and Applications (pp. 119–147). IGI Global. https://doi.org/10.4018/978-1-6684-3662-2.ch006
Rao, N. T., Neal Joshua, E. S., & Bhattacharyya, D. (2022). An Extensive Discussion on Utilization of Data Security and Big Data Models for Resolving Healthcare Problems. Multi-Chaos, Fractal and Multi-Fractional Artificial Intelligence of Different Complex Systems, 19, 311–324. https://doi.org/10.1016/b978-0-323-90032-4.00001-8
Santoso, J. T., Wibowo, M. C., & Raharjo, B. (2024). Optimizing Deep Learning Efficiency through Algorithm-Hardware Co-design. Journal of Advances in Information Technology, 15(10), 1163–1173. https://doi.org/10.12720/jait.15.10.1163-1173
Shabbir, N., Kütt, L., Jawad, M., Husev, O., Rehman, A. U., Gardezi, A. A., Shafiq, M., & Choi, J. G. (2022). Short-Term Wind Energy Forecasting Using Deep Learning-Based Predictive Analytics. Computers, Materials and Continua, 72(1), 1017–1033. https://doi.org/10.32604/cmc.2022.024576
Strielkowski, W., Vlasov, A., Selivanov, K., Muraviev, K., & Shakhnov, V. (2023). Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review. Energies, 16(10), 4025. https://doi.org/10.3390/en16104025
Yin, L., Li, B., Li, P., & Zhang, R. (2023). Research on Stock Trend Prediction Method Based on Optimized Random Forest. CAAI Transactions on Intelligence Technology, 8(1), 274–284. https://doi.org/10.1049/cit2.12067
Zagajewski, B., Kluczek, M., Raczko, E., Njegovec, A., Dabija, A., & Kycko, M. (2021). Comparison of Random Forest, Support Vector Machines, and Neural Networks for Post-Disaster Forest Species Mapping of the Krkonoše/Karkonosze Transboundary Biosphere Reserve. Remote Sensing, 13(13), 2581. https://doi.org/10.3390/rs13132581
Zhang, Y., & Liu, J. (2022). Prediction of Overall Energy Consumption of Data Centers in Different Locations. Sensors, 22(10), 3704. https://doi.org/10.3390/s22103704
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