GREEN COMPUTING: ENERGY-EFFICIENT ALGORITHMS AND DATA CENTERS

Abstract

The exponential growth of data center operations and cloud computing infrastructure has resulted in unprecedented energy consumption, contributing significantly to global carbon emissions and environmental degradation. This paper presents a comprehensive investigation into energy-efficient algorithms and sustainable data center architectures as critical components of green computing. Existing energy optimization approaches including Dynamic Voltage and Frequency Scaling (DVFS), virtualization technologies, AI-driven workload distribution, and advanced cooling systems are analyzed in relation to their role in reducing data center power demand. A conceptual Energy-Aware Data Processing (EADP) algorithm is presented by integrating data management, task scheduling, and hardware optimization techniques derived from current literature. Simulated comparative results indicate meaningful reductions in energy consumption, improvements in processing time, and better Power Usage Effectiveness (PUE) and Carbon Usage Effectiveness (CUE) values under an energy-aware operating model. The study argues that energy-efficient algorithms combined with sustainable infrastructure practices provide a viable pathway toward environmentally responsible digital transformation.[1][2][3][4][5][6][7][8] Keywords: green computing; energy-efficient algorithms; data centers; DVFS; PUE; sustainable computing; renewable energy