Green Computing Optimization for Multi-Region Streaming Platforms

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Sravya Sambaturu

Abstract

Global streaming services are always under pressure to control their resource usage effectively while still offering millions of users worldwide flawless experiences. As user demands for continuous, high-quality content increase, it is becoming increasingly important to strike the right balance between low latency, high availability, and resource efficiency. However, the infrastructure required to meet these demands often results in significant energy consumption and operational costs, presenting a major challenge for sustainability. The application of green computing principles to multi-region cloud infrastructure optimization is examined in this article, with an emphasis on tactics that minimize energy consumption and operational costs without compromising the performance that users rely on. Platforms can preserve their competitive advantage while making significant progress toward environmental responsibility by implementing more intelligent, energy-efficient procedures.

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Green Computing Optimization for Multi-Region Streaming Platforms (Sravya Sambaturu , Trans.). (2025). International Journal of Emerging Science and Engineering (IJESE), 13(2), 27-29. https://doi.org/10.35940/ijese.F3653.13020125
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How to Cite

Green Computing Optimization for Multi-Region Streaming Platforms (Sravya Sambaturu , Trans.). (2025). International Journal of Emerging Science and Engineering (IJESE), 13(2), 27-29. https://doi.org/10.35940/ijese.F3653.13020125
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References

Reddy, S. P., & Chandan, H. K. S. (2014, February). Energy-aware scheduling of real-time and non-real-time tasks on cloud processors (Green Cloud Computing). International Conference on Information Communication and Embedded Systems (ICICES2014). DOI: https://doi.org/10.1109/ICICES.2014.7033827

Shailesh S. Deore, Ashok N. Patil, and Ruchira Bhargava. 2012. Energy-efficient scheduling scheme for virtual machines in cloud computing. International Journal of Computer Applications 56, 10 (Oct. 2012). DOI: https://doi.org/10.5120/8926-2999

J Kavitha, Rokesh Kumar Yarava, Energy Efficiency Analysis Between Green Computing and Cloud Computing. (2019). In International Journal of Recent Technology and Engineering (Vol. 8, Issue 3S2, pp. 254–258). DOI: https://doi.org/10.35940/ijrte.C1047.1083S219

Jain, J. (2019). Modern with Advanced Direction in Green Cloud. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 2, pp. 3090–3095). DOI: https://doi.org/10.35940/ijeat.F9184.129219

Vatsal, S., & Verma, Dr. S. B. (2023). Data Centre Efficiency Enhancement by Metrics Oriented Approach to Revamp Green Cloud Computing Concept. In International Journal of Innovative Technology and Exploring Engineering (Vol. 12, Issue 8, pp. 1–14). DOI: https://doi.org/10.35940/ijitee.F9532.0712823

Naik, O., Jaiswal, S., Bhuyar, Dr. K., & Kodape, Dr. S. (2023). Green Hydrogen Production as a Sustainable Initiative for Alternative Energy Source: A Review. In International Journal of Basic Sciences and Applied Computing (Vol. 9, Issue 10, pp. 1–5). DOI: https://doi.org/10.35940/ijbsac.I0503.0691023

Paul, D., Pal, O. K., Islam, Md. M., Mohammad, M., & Babu, R. M. (2023). Design and Implementation of an Efficient Smart Digital Energy Meter. In International Journal of Soft Computing and Engineering (Vol. 13, Issue 1, pp. 25–30). DOI: https://doi.org/10.35940/ijsce.A3600.0313123