Service Architecture Optimization and Resource Scheduling in Big Data Processing Platforms

Article Sidebar

PDF
Published: 2026-05-24
Keywords:
Service Architecture, Resource Scheduling, Big Data Processing, Optimization, Scalability

Main Article Content

Christopher Alexander Williams
Faculty of Engineering, Cebu Technological University, Cebu, Philippines
Jonathan Benedict Fernandez
Faculty of Engineering, Cebu Technological University, Cebu, Philippines

Abstract

This research article explores the optimization of service architecture and resource scheduling in big data processing platforms. It begins by identifying the challenges associated with managing large-scale data workflows, including latency, resource allocation, and system scalability. A novel framework is proposed to enhance service architecture through modular design and dynamic resource scheduling algorithms. The study employs a mixed-methods approach, combining simulation-based experiments with analytical modeling to evaluate performance metrics such as throughput, latency, and resource utilization. Results demonstrate significant improvements in processing efficiency and scalability when compared to traditional architectures. The discussion highlights the practical implications of the findings for real-world big data platforms and outlines future research directions.

Article Details

Issue

Vol. 2 No. 1 (2026)

Section

Articles

How to Cite

Service Architecture Optimization and Resource Scheduling in Big Data Processing Platforms. (2026). Hua Xia Xin Zhi, 2(1), 182-194. https://journals.hubblepress.com/index.php/HXXZ/article/view/39

References

1. Z. H. Zhan, X. F. Liu, Y. J. Gong, J. Zhang, H. S. H. Chung, and Y. Li, "Cloud computing resource scheduling and a survey of its evolutionary approaches," ACM Comput. Surv., vol. 47, no. 4, pp. 1-33, 2015.

2. C. L. Cheong, "Study on Risk Assessment Methods and Multi-Dimensional Control Mechanisms in AI Systems," Eur. J. AI, Comput. & Inf., vol. 2, no. 1, pp. 31-46, Jan. 2026, doi: 10.71222/58dr7v22.

3. G. Ying, "Study on uncertainty data analysis for common natural disaster prediction in the US using cloud computing and machine learning," Journal of Science, Innovation & Social Impact, vol. 2, no. 1, pp. 178-189, 2026.

4. P. Senkul and I. H. Toroslu, "An architecture for workflow scheduling under resource allocation constraints," Inf. Syst., vol. 30, no. 5, pp. 399-422, 2005.

5. Z. Gao, "A Review of Integrated Artificial Intelligence and Big Data Analytics Models for Intelligent Decision-Making," Eur. J. AI, Comput. & Inf., vol. 2, no. 2, pp. 38-46, 2026.

6. Y. Zhao, R. N. Calheiros, G. Gange, K. Ramamohanarao, and R. Buyya, "SLA-based resource scheduling for big data analytics as a service in cloud computing environments," in Proc. 44th Int. Conf. Parallel Process., 2015, pp. 510-519.

7. B. Li, "Beyond Intuition: Data-Driven Business Strategists and the Transformation of Strategic Decision-Making," Artif. Intell. & Digit. Technol., vol. 3, no. 1, pp. 1-9, 2026.

8. 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.

9. Z. Gao, "Artificial intelligence techniques for complex big data environments: Methods and perspectives," Advances in Engineering Innovation, vol. 16, no. 7, pp. 167-170, 2025.

10. K. R. Lee, M. H. Fu, and Y. H. Kuo, "A hierarchical scheduling strategy for the composition services architecture based on cloud computing," in Proc. 2nd Int. Conf. Next Gener. Inf. Technol., 2011, pp. 163-169.

11. R. Buyya, D. Abramson, and J. Giddy, "Nimrod/G: An architecture for a resource management and scheduling system in a global computational grid," in Proc. 4th Int. Conf./Exhib. High Perform. Comput. Asia-Pacific Reg., vol. 1, 2000, pp. 283-289.

12. H. Zhu, Y. Wang, X. Hei, W. Ji, and L. Zhang, "A blockchain-based decentralized cloud resource scheduling architecture," in Proc. Int. Conf. Netw. Netw. Appl. (NaNA), 2018, pp. 324-329.

13. J. Li, "Resource optimization scheduling and allocation for hierarchical distributed cloud service system in smart city," Future Gener. Comput. Syst., vol. 107, pp. 247-256, 2020.

14. J. Wang, P. Korambath, I. Altintas, J. Davis, and D. Crawl, "Workflow as a service in the cloud: architecture and scheduling algorithms," Procedia Comput. Sci., vol. 29, pp. 546-556, 2014.

15. 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.