Digital Waste Mitigation in AI and Cloud Computing: A Comprehensive Framework for Environmental Sustainability
DOI:
https://doi.org/10.32628/CSEIT251112147Keywords:
digital waste, artificial intelligence, cloud computing, environmental sustainability, green computingAbstract
This article examines the growing environmental impact of digital waste generated by artificial intelligence systems and cloud computing infrastructure. It presents a comprehensive article analysis of the environmental footprint associated with data centers, AI model training, and cloud-based operations. The article introduces a framework for identifying and measuring digital waste across computing environments, followed by an evaluation of current mitigation strategies including smart data management, deduplication techniques, and energy-efficient algorithm design. Through case studies and empirical analysis, the article demonstrates the effectiveness of combining technical solutions with organizational policies to reduce digital waste. The article highlights the crucial role of edge computing and renewable energy adoption in minimizing environmental impact. Additionally, the article proposes a set of best practices for organizations to integrate sustainability into their digital transformation initiatives. This article contributes to the growing body of literature on sustainable computing by providing actionable strategies for balancing technological advancement with environmental preservation. The conclusions emphasize the importance of industry-wide collaboration in establishing standards for measuring and reducing digital waste, while highlighting areas for future research in green computing technologies.
Downloads
References
B. van Gils and H. Weigand, "Towards Sustainable Digital Transformation," 2020 IEEE 22nd Conference on Business Informatics (CBI), Antwerp, Belgium, 2020, pp. 21-29. https://ieeexplore.ieee.org/document/9140252/citations#citations
R. Hasan and R. Burns, "The life and death of unwanted bits: Towards proactive waste data management in digital ecosystems," Third International Conference on Innovative Computing Technology (INTECH 2013), London, UK, 2013, pp. 158-162. https://ieeexplore.ieee.org/document/6653665/figures#figures
R. Ramasamy, R. S. Gopi, and R. Palanisamy, "E-waste Management in the Digital Era: A Sustainable Computing Approach," IEEE Potentials Magazine, vol. 43, no. 2, pp. 18-25, March 2024. https://potentialsmagazine.ieee.org/2024/03/06/e-waste-management-in-the-digital-era-a-sustainable-computing-approach-climate-change/
Y. Zhang and X. Shen, "Quantitative Analysis on Geometric Size of LiDAR Footprint," IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 3, pp. 487-490, 26 August 2013. https://ieeexplore.ieee.org/abstract/document/6587124
L. A. Torres, C. J. Barrios, and Y. Denneulin, "Computational Resource Consumption in Convolutional Neural Network Training – A Focus on Memory," Supercomputing Frontiers and Innovations, 8(1), 356, 2021. https://superfri.org/index.php/superfri/article/view/356
F. Xu et al., "iGniter: Interference-Aware GPU Resource Provisioning for Predictable DNN Inference in the Cloud," IEEE Transactions on Parallel and Distributed Systems, 34(3), pp. 812-827, 2023. https://par.nsf.gov/servlets/purl/10431787
Masanet, E., Shehabi, A., Lei, N. et al., "Recalibrating global data center energy-use estimates," Science, vol. 367, no. 6481, pp. 984-986, 2020. https://www.science.org/doi/10.1126/science.aba3758
M. Brugnara et al., "Data Management and Smart Cities," IEEE Smart Cities White Paper, 2019. https://smartcities.ieee.org/images/files/pdf/2019-01_SCWhitePaper-DataManagementinSC.pdf
N. Chhabra and M. Bala, "A Comparative Study of Data Deduplication Strategies," IEEE Conference Publication, 2019. https://ieeexplore.ieee.org/abstract/document/8703363
L. Lakhani, "Green Computing - A New Trend in IT," International Journal of Scientific Research in Computer Science and Engineering, vol. 4, no. 3, pp. 11-13, Jun. 2016. https://www.isroset.org/journal/IJSRCSE/full_paper_view.php?paper_id=271
M. Giacobbe, A. Celesti, M. Fazio, M. Villari, and A. Puliafito, "An approach to reduce carbon dioxide emissions through virtual machine migrations in a sustainable cloud federation," 2015. https://ieeexplore.ieee.org/document/7101383
T. Qiu, J. Chi, X. Zhou. er al., and M. Atiquzzaman, "Edge Computing in Industrial Internet of Things: Architecture, Advances and Challenges," 2020. https://ieeexplore.ieee.org/abstract/document/9139976/citations#citations
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.