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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
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ISO Certification: 9001:2015
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License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 7

Published on: July 2026

GREEN COMPUTING: ENERGY-EFFICIENT ALGORITHMS AND DATA CENTERS

Shankar Kumar

Haspura High School, Haspura - Aurangabad (Bihar)
DOI:https://doi.org/10.55041/ijcope.v2i7.158

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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

How to Cite this Paper

Kumar, S. (2026). Green Computing: Energy-Efficient Algorithms and Data Centers. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(7), 1-9. https://doi.org/10.55041/ijcope.v2i7.158

Kumar, Shankar. "Green Computing: Energy-Efficient Algorithms and Data Centers." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 7, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijcope.v2i7.158.

Kumar, Shankar. "Green Computing: Energy-Efficient Algorithms and Data Centers." International Journal of Creative and Open Research in Engineering and Management 02, no. 7 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijcope.v2i7.158.

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References


  1. International Energy Agency (IEA). (2020). Data Centre and Data Transmission Network Energy Use. https://www.iea.org

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  5. Fernandes, R., &Fernandes, L. (2015). Green task scheduling algorithms for cloud data centers. IJRET: International Journal of Research in Engineering and Technology, 4(3), 245-249.

  6. Li, K., & Li, Y. (2016). Energy-efficient data compression for big data processing. IEEE Transactions on Parallel and Distributed Systems, 27(10), 2836-2848.

  7. Wang, X., & Li, K. (2014). A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters. Future Generation Computer Systems, 37, 141-147.

  8. Zhang, Y., & Li, K. (2018). Energy-efficient hardware optimization for big data processing. Journal of Supercomputing, 74(1), 1-16.

  9. (2025). Green Computing: Advancing Energy-Efficient Data Centers With AI. International Journal of Engineering Studies, 11(3), 3846. https://theaspd.com/index.php/ijes/article/view/3846

  10. International Energy Agency. (2020). Data Centre and Data Transmission Network Energy Use. https://www.iea.org/reports

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  • •Peer Review Type: Double-Blind Peer Review
  • •Published on: Jul 14 2026
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