Publication

Abstract : Energy conservation in cloud data centers remains one of the biggest research challenges today. Energy efficiency has become an important concern in the management of contemporary data centers due to the rapidly growing computational needs and the environmental impact of power consumption. This study examines various power management techniques, including Dynamic Voltage and Frequency Scaling (DVFS), Dynamic Power Management (DPM), and Adaptive Voltage Scaling (AVS), to optimize CPU power consumption. Using frequency data from historical and current CPU usage, these algorithms control CPU frequency settings and assess their impact on energy consumption and performance. The results indicate that DVFS reduces power consumption by 25–30%, DPM achieves energy savings of 28–35%, and AVS provides savings of 35–40% by dynamically adjusting both voltage and frequency. A performance matrix evaluates the power savings and utilization efficiency of these strategies to determine the most suitable approach. AVS was found to be 5–10% more energy efficient than DVFS and DPM, demonstrating its advantage in real-world applications. Furthermore, AVS exhibited the highest precision (96%) to adapt to workload fluctuations, compared to 95% for DVFS and 92% for DPM. This study focuses on adaptive power management and provides key findings on algorithmic solutions for energy efficiency in software-defined cloud infrastructures. The findings contribute to reducing data center energy consumption while maintaining performance, aligning with the UN Sustainable Development Goals by promoting sustainable and eco-friendly cloud operations.