Abstract

Cloud systems consume enormous amounts of energy and infrastructure resources that resulting in high operating costs and carbon dioxide emissions. Managing energy and infrastructure resources are the most important issue in cloud. high energy consumption in cloud data centers has become one of the primary challenges. The goal of this research is to present an Energy Aware Genetic Algorithm (EAGA) to optimize energy consumption and enhance resource efficiency in cloud environments. The proposed algorithm was implemented using simulations in real cloud environments, such as Amazon EC2 and Planet Lab. In this process, the proposed algorithm was compared with traditional algorithms like PSO, and three main metrics, including energy consumption, execution time, and resource efficiency, were evaluated. Simulation results showed that the proposed algorithm was able to reduce energy cost by 15.8%, decrease task execution time by 14.6%, and increase resource efficiency by 10.8% as Fig. 2, 3, 4, and Table2.

References

• Amir Javadpour, et al.,An Energy-optimized Embedded load balancing using DVFS computing in Cloud Data centers. Computer Communications, Volume 197, 1 January (2023), Pages 255-266.
• Singhal, S., Gupta, N., Berwal, P., Naveed, Q. N., Lasisi, A., & Wodajo, A. W. (2023). Energy efficient resource allocation in cloud environment using metaheuristic algorithm. IEEE Access, 11, 126135-126146.
• Abdel-Basset, M., El-Shahat, D., Elhoseny, M., & Song, H. (2020). Energy-aware metaheuristic algorithm for industrial-Internet-of-Things task scheduling problems in fog computing applications. IEEE Internet of Things Journal, 8(16), 12638-12649.
• Ankit, Amritpal Singh., Performance-driven resource optimization strategies and allocation policies for virtualized cloud environments, J. Integr. Sci. Technol., 2026, 14(3), 1531.
• Mirmohseni, S. M., Tang, C., & Javadpour, A. (2022). FPSO-GA: a fuzzy metaheuristic load balancing algorithm to reduce energy consumption in cloud networks. Wireless Personal Communications, 127(4), 2799-2821.
• Jomah, S. (2024, October). Meta-Heuristic Scheduling: A Review on Swarm Intelligence and Hybrid Meta-Heuristics Algorithms for Cloud Computing. In Operations Research Forum (Vol. 5, No. 4, p. 94). Cham: Springer International Publishing.
• Virtual machine consolidation in cloud computing systems: Challenges and future trends R Zolfaghari, AM Rahmani, Wireless Personal Communications 115 (3), 2289-2326.
• Application of virtual machine consolidation in cloud computing systems R Zolfaghari, A Sahafi, AM Rahmani, R Rezaei Sustainable Computing: Informatics and Systems 30, 100524.
• An energy-aware virtual machines consolidation method for cloud computing: Simulation and verification, R Zolfaghari, A Sahafi, AM Rahmani, R Rezaei Software: Practice and Experience 52 (1), 194-235.
• Energy-performance aware virtual machines migration in cloud network by using prediction and fuzzy approaches, Rahmat Zolfaghari, Engineering Applications of Artificial Intelligence 131 (2024) 107825.
• Prity, F. S., Uddin, K. A., & Nath, N. (2024). Exploring swarm intelligence optimization techniques for task scheduling in cloud computing: algorithms, performance analysis, and future prospects. Iran Journal of Computer Science, 7(2), 337-358.
• M. A. Khameneh and R. Zolfaghari, "Consensus Process in Large-Scale Group Decision-Making Based on Limited Trust and Social Networks Using Machine Learning," 2025 11th International Conference on Web Research (ICWR), Tehran, Iran, Islamic Republic of, 2025, pp. 116-122, doi: 10.1109/ICWR65219.2025.11006183. [13} E. A. Abyaneh, R. Zolfaghari and A. A. Abyaneh, "User Intent Detection in Persian Text-Based Chatbots: A Comprehensive Review of Methods and Challenges," 2025 11th International Conference on Web Research (ICWR), Tehran, Iran, Islamic Republic of, 2025, pp. 243-249, doi: 10.1109/ICWR65219.2025.11006173.
• N. Eizadishad, R. Zolfaghari and J. Mohammadzadeh, "A Dynamic System for Classification and Detection Liver Abnormal Genes Using Neural Networks," 2026 International Biennial Conference on Advances in Artificial Intelligence and Data Science (IBAAIDS), Bushehr, Iran, Islamic Republic of, 2026, pp. 1-4, doi: 10.1109/IBAAIDS66771.2026.11567406.