Energy Consumption Optimization in Data Centers Using LSTM-Based Load Prediction and Dynamic Resource Allocation

[1] Goldman Sachs. (2023). AI poised to drive 160% increase in power demand. Goldman Sachs Insights. Retrieved from https://www.goldmansachs.com/insights/articles/AI-poised-to-drive-160-increase-in-power-demand. Search in Google Scholar

[2] Sherstinsky, A. (2020). Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena, 404, 132306. Search in Google Scholar

[3] Velasco, L. C. P., Polestico, D. L. L., Macasieb, G. P. O., Reyes, M. B. V., & Vasquez Jr, F. B. (2018). Load forecasting using autoregressive integrated moving average and artificial neural network. International Journal of Advanced Computer Science and Applications, 9(7), 23-29. Search in Google Scholar

[4] Zhou, Y., Wang, L., & Qian, J. (2022). Application of combined models based on empirical mode decomposition, deep learning, and autoregressive integrated moving average model for short-term heating load predictions. Sustainability, 14(12), 7349. Search in Google Scholar

[5] Hermias, J. P., Teknomo, K., & Monje, J. C. N. (2017, December). Short-term stochastic load forecasting using autoregressive integrated moving average models and Hidden Markov Model. In 2017 International Conference on Information and Communication Technologies (ICICT) (pp. 131-137). IEEE. Search in Google Scholar

[6] Chen, D., & York, M. (2008, April). Neural network based very short term load prediction. In 2008 IEEE/PES Transmission and Distribution Conference and Exposition (pp. 1-9). IEEE. Search in Google Scholar

[7] Zhu, J., Jiang, Q., Shen, Y., Qian, C., Xu, F., & Zhu, Q. (2022). Application of recurrent neural network to mechanical fault diagnosis: A review. Journal of Mechanical Science and Technology, 36(2), 527-542. Search in Google Scholar

[8] Hijji, M., Ahmad, B., Alam, G., Alwakeel, A., Alwakeel, M., Abdulaziz Alharbi, L., ... & Khan, M. U. (2022). Cloud servers: Resource optimization using different energy saving techniques. Sensors, 22(21), 8384. Search in Google Scholar

[9] Javadpour, A., Sangaiah, A. K., Pinto, P., Ja’fari, F., Zhang, W., Abadi, A. M. H., & Ahmadi, H. (2023). An energy-optimized embedded load balancing using DVFS computing in cloud data centers. Computer Communications, 197, 255-266. Search in Google Scholar

[10] Udayasankaran, P., & Thangaraj, S. J. J. (2023). Energy efficient resource utilization and load balancing in virtual machines using prediction algorithms. International Journal of Cognitive Computing in Engineering, 4, 127-134. Search in Google Scholar

[11] Du, Y., Zhou, Z., Yang, X., Yang, X., Wang, C., Liu, J., & Yuan, J. (2023). Dynamic thermal environment management technologies for data center: A review. Renewable and Sustainable Energy Reviews, 187, 113761. Search in Google Scholar

[12] Babu, V. V., Roselyn, J. P., & Sundaravadivel, P. (2023). Multi-objective genetic algorithm based energy management system considering optimal utilization of grid and degradation of battery storage in microgrid. Energy Reports, 9, 5992-6005. Search in Google Scholar

[13] Peng, X., Chen, H., & Guan, C. (2023). Energy management optimization of fuel cell hybrid ship based on particle swarm optimization algorithm. Energies, 16(3), 1373. Search in Google Scholar

[14] Nayak, P. C., Mishra, S., Prusty, R. C., & Panda, S. (2023). Hybrid whale optimization algorithm with simulated annealing for load frequency controller design of hybrid power system. Soft Computing, 1-24. Search in Google Scholar

[15] Ding, Z., Tian, Y. C., Wang, Y. G., Zhang, W. Z., & Yu, Z. G. (2023). Accelerated computation of the genetic algorithm for energy-efficient virtual machine placement in data centers. Neural Computing and Applications, 35(7), 5421-5436. Search in Google Scholar

[16] Sirisumrannukul, S., Intaraumnauy, T., & Piamvilai, N. (2024). Optimal control of cooling management system for energy conservation in smart home with ANNs-PSO data analytics microservice platform. Heliyon, 10(6). Search in Google Scholar

[17] Mao, L., Chen, R., Cheng, H., Lin, W., Liu, B., & Wang, J. Z. (2023). A resource scheduling method for cloud data centers based on thermal management. Journal of Cloud Computing, 12(1), 84. Search in Google Scholar

[18] Ghandour, O., El Kafhali, S., & Hanini, M. (2024). Adaptive workload management in cloud computing for service level agreements compliance and resource optimization. Computers and Electrical Engineering, 120, 109712. Search in Google Scholar

[19] Tarmanini, C., Sarma, N., Gezegin, C., & Ozgonenel, O. (2023). Short term load forecasting based on ARIMA and ANN approaches. Energy Reports, 9, 550-557. Search in Google Scholar

[20] Khani, M., Sadr, M. M., & Jamali, S. (2024). Deep reinforcement learning‐based resource allocation in multi‐access edge computing. Concurrency and Computation: Practice and Experience, 36(15), e7995. Search in Google Scholar