Authors : FNU Pawan Kumar

Volume/Issue : Volume 10 - 2025, Issue 6 - June

Google Scholar : https://tinyurl.com/36yn5fe9

DOI : https://doi.org/10.38124/ijisrt/25jun1313

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Abstract : The exponential growth of digital commerce and online services has driven an urgent need for scalable and resilient architectures capable of handling high-volume order processing. Microservices architecture, in combination with cloud-native technologies, has emerged as a promising solution, enabling modular design, independent scaling, and fault isolation. This paper reviews the current state-of-the-art in scalable microservices for cloud-based order processing, highlighting architectural patterns, orchestration strategies, observability mechanisms, and AI-driven automation. Experimental results demonstrate significant improvements in throughput, latency, and reliability compared to monolithic architectures. However, challenges such as service orchestration complexity, data consistency, and intelligent scaling remain areas of ongoing research. This study concludes with future directions, including the integration of autonomous orchestration, edge-cloud synergy, and enhanced observability frameworks. By addressing these challenges, microservices architecture can unlock new possibilities for mission-critical, high-volume order processing in dynamic cloud environments [10][11][12][13][14][15].

Keywords : Microservices Architecture; Scalable Systems; Cloud-Native Environments; High-Volume Order Processing.

References :

1. Newman, S. (2015). Building microservices: Designing fine-grained systems. O'Reilly Media.
2. Chen, L., & Bahsoon, R. (2015). Self-adaptive and self-aware cloud-based systems. Future Generation Computer Systems, 48, 59-76.
3. Satyanarayanan, M. (2017). The emergence of edge computing. Computer, 50(1), 30-39.
4. Bernstein, D. (2014). Containers and cloud: From LXC to Docker to Kubernetes. IEEE Cloud Computing, 1(3), 81-84.
5. Gellings, C. W. (2013). The smart grid: Enabling energy efficiency and demand response. The Fairmont Press.
6. Ghosh, R., & Dasgupta, D. (2021). A survey of AI and edge computing integration in IoT. IEEE Transactions on Industrial Informatics, 17(6), 4032-4042.
7. Pahl, C., & Jamshidi, P. (2016). Microservices: A systematic mapping study. Software Architecture, 67, 1-10.
8. Villamizar, M., Garcés, O., Castro, H., Verano, M., Salamanca, L., Casallas, R., & Gil, S. (2016). Evaluating the monolithic and the microservice architecture pattern to deploy web applications in the cloud. 2016 10th Computing Colombian Conference (10CCC), 1-6.
9. Lee, S., & Kumar, A. (2022). Adaptive resource allocation for cloud-based microservices. Future Generation Computer Systems, 129, 202–215.
10. Li, H., & Zhou, R. (2025). Towards autonomous microservices architectures: AI-driven scalability and resilience. ACM Computing Surveys, 58(1), 1-30.
11. Zhao, Y., & Wang, P. (2020). Orchestrating microservices: State-of-the-art and research challenges. IEEE Access, 8, 100451-100470.
12. Kim, J., & Singh, A. (2023). Observability in cloud-native microservices architectures. Journal of Systems and Software, 199, 111432.
13. Patel, M., & Chen, Y. (2024). Service meshes: Enabling scalable and secure microservices. IEEE Internet Computing, 28(2), 22-31.
14. Gannon, D., Barga, R., & Sundaram, H. (2018). Cloud-native computing: Cloud-based systems for scalable and flexible microservices architectures. Communications of the ACM, 61(7), 44-52.
15. Buyya, R., & Dastjerdi, A. V. (2016). Internet of Things: Principles and paradigms. Morgan Kaufmann.
16. Armbrust, M., Stoica, I., Zaharia, M., & Fox, A. (2019). A view of cloud computing. Communications of the ACM, 53(4), 50-58.
17. Dragoni, N., Giallorenzo, S., Lafuente, A. L., Mazzara, M., Montesi, F., Mustafin, R., & Safina, L. (2017). Microservices: Yesterday, today, and tomorrow. Present and Ulterior Software Engineering, 195-216.
18. Fielding, R. T. (2000). Architectural styles and the design of network-based software architectures (Doctoral dissertation). University of California, Irvine.
19. Adzic, G., & Chatley, R. (2017). Serverless computing: Economic and architectural impact. Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, 884-889.
20. Amin, M. T., & Bhatti, S. N. (2017). Scaling microservices in the cloud: Load balancing with Docker Swarm and Kubernetes. Cloud Computing Conference, 1-6.
21. Namiot, D., & Sneps-Sneppe, M. (2014). On micro-services architecture. International Journal of Open Information Technologies, 2(9), 24-27.
22. Turnbull, J. (2014). The Docker book: Containerization is the new virtualization. James Turnbull.
23. Chacon, S., & Straub, B. (2014). Pro Git. Apress.
24. Daigneau, R. (2012). Service design patterns: Fundamental design solutions for SOAP/WSDL and RESTful web services. Addison-Wesley.
25. Taibi, D., & Lenarduzzi, V. (2018). On the definition of microservice bad smells. IEEE Software, 35(3), 56-62.