Sustainable System Architecture Design for Big Data Cloud Platforms

Main Article Content

Sebastian Reinhardt

Abstract

This research article explores sustainable system architecture design for big data cloud platforms, emphasizing scalability, energy efficiency, and data processing optimization. The study introduces a novel framework that integrates modular design principles with advanced resource allocation algorithms to enhance performance while minimizing environmental impact. Experimental evaluations demonstrate significant improvements in computational efficiency and energy consumption compared to traditional architectures. The findings contribute to the growing field of sustainable computing by providing actionable insights for designing eco-friendly cloud platforms capable of handling large-scale data operations.

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How to Cite

Sustainable System Architecture Design for Big Data Cloud Platforms. (2026). Hua Xia Xin Zhi, 2(1), 254-266. https://journals.hubblepress.com/index.php/HXXZ/article/view/46

References

1. A. Sinaeepourfard, S. Shaik, and N. Mesgaribarzi, "Decentralized, distributed, and hybrid ICT architectures: Hierarchical multitier big data driven management for smart, sustainable, scalable and reliable cities," in 2024 IEEE Conference on Technologies for Sustainability (SusTech), 2024, pp. 345-355.

2. A. D. Giordano, *Data integration blueprint and modeling: techniques for a scalable and sustainable architecture*. Pearson Education, 2010.

3. J. Wu et al., "Building an accessible, usable, scalable, and sustainable service for scholarly big data," in 2021 IEEE International Conference on Big Data (Big Data), 2021, pp. 141-152.

4. P. Shen, "Service architecture and optimization strategies in cloud-based big data platforms," Journal of Science, Innovation & Social Impact, vol. 2, no. 1, pp. 288-298, 2026.

5. Y. Zhang, S. Ren, Y. Liu, and S. Si, "A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products," Journal of Cleaner Production, vol. 142, pp. 626-641, 2017.

6. C. Stergiou, K. E. Psannis, B. B. Gupta, and Y. Ishibashi, "Security, privacy & efficiency of sustainable cloud computing for big data & IoT," Sustainable Computing: Informatics and Systems, vol. 19, pp. 174-184, 2018.

7. J. Zhao, Y. Liu, and P. Zhou, "Framing a sustainable architecture for data analytics systems: An exploratory study," IEEE Access, vol. 6, pp. 61600-61613, 2018.

8. G. Ying, "Research on a Machine Learning and Cloud Computing-Based System for Real-Time Prediction, Fast Decision-Making, and Dynamic Resource Scheduling in Large-Scale Networks," 2025 IEEE 4th International Conference of Safe Production and Informatization (IICSPI), Chongqing, China, 2025, pp. 558-564, doi: 10.1109/IICSPI66775.2025.11438124.

9. P. Shen, "System architecture design of cloud platforms for large-scale data processing," Journal of Sustainability, Policy, and Practice, vol. 2, no. 2, pp. 67-77, 2026.

10. C. Li et al., "Towards sustainable in-situ server systems in the big data era," ACM SIGARCH Computer Architecture News, vol. 43, no. 3S, pp. 14-26, 2015.

11. R. Saadane, A. Chehri, and M. Wahbi, "6G enabled smart environments and sustainable cities: An intelligent big data architecture," in 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), 2022, pp. 1-5.

12. F. Lucivero, "Big data, big waste? A reflection on the environmental sustainability of big data initiatives," Science and Engineering Ethics, vol. 26, no. 2, pp. 1009-1030, 2020.

13. B. Li, "Reframing Business Strategy through Data: A Review of Data-Driven Strategic Thinking," J. Sustain., Policy, & Pract., vol. 2, no. 1, pp. 230-244, 2026.

14. N. Stefanovic, M. Radenkovic, Z. Bogdanovic, J. Plasic, and A. Gaborovic, "Adaptive cloud-based big data analytics model for sustainable supply chain management," Sustainability, vol. 17, no. 1, p. 354, 2025.