Shahid Rajaee Teacher Training University Journal of Electrical and Computer Engineering Innovations (JECEI) 2322-3952 Articles in Press 2025 10 12 Smart Scheduling for Green Cloud Data Centers: Reducing Energy Consumption through Innovative Algorithms 2427 10.22061/jecei.2025.12132.855 EN Gowri S Department of Computer Science ,K. S. Rangasamy College of Arts and Science (Autonomous), Tiruchengode, Tamil Nadu, India Jaganathan Rathi Department of Computer Science, , K. S. Rangasamy College of Arts and Science(Autonomous), Tiruchengode, Tamil Nadu, India. Journal Article 2025 06 18 <strong>Background and Objectives: </strong>Cloud computing can play a vital role in promoting environmental sustainability by leveraging eco-friendly dedicated servers that adhere to green computing standards. The concept of "green cloud computing" revolves around harnessing cutting-edge technologies to minimize the environmental footprint of computing systems. One of the significant challenges in cloud-based systems is task scheduling, which must be optimized to enhance system efficiency, user experience, and environmental sustainability.<br /><strong>Method:</strong> This paper proposes a novel Hybrid HEES (Hierarchical Energy-Efficient Scheduling) method that optimizes energy consumption and task scheduling in cloud computing environments. By combining genetic algorithm optimization, workflow-based scheduling, and energy-aware resource allocation, HEES achieves significant reductions in energy consumption and average task completion time.<br /><strong>Results:</strong> The method is evaluated through simulations, demonstrating its effectiveness in optimizing energy efficiency and task scheduling performance. The Hybrid HEES method has the potential to reduce energy consumption, improve computing performance, and enhance sustainability in cloud computing environments.<br /><strong>Conclusion: </strong>To evaluate a proposed HEES method through cloudsim 3.0 simulations, the numerical results confirm the effectiveness of HEES algorithm, which achieves average Energy consumption performance improvements of around 12% compared to GP and 8% compared to RR existing methods.