Cloud Native Microservices Architecture for Scalable and Resilient Enterprise Application Deployment
Abstract
Modern enterprise applications are increasingly required to handle large-scale user demands, high availability requirements, and rapidly changing business environments. Traditional monolithic application architectures struggle to meet these demands due to limited scalability, tight coupling of components, and difficulties in continuous deployment. Cloud-native microservices architecture has emerged as a powerful paradigm that decomposes enterprise applications into small, independent, and loosely coupled services that can be developed, deployed, and scaled independently. This research proposes a Cloud-Native Microservices Architecture for scalable and resilient enterprise application deployment designed to improve system flexibility, fault tolerance, and operational efficiency. The proposed architecture leverages containerization technologies such as Docker and orchestration platforms such as Kubernetes to manage service deployment, scaling, and monitoring in cloud environments. It also integrates service discovery, API gateways, distributed logging, and load balancing mechanisms to ensure seamless communication between microservices. Resilience is enhanced through fault isolation, circuit breakers, and auto-scaling strategies. Experimental evaluation is conducted using a simulated cloud environment to analyze scalability, response time, system availability, and failure recovery performance. The results demonstrate that the proposed architecture significantly improves application scalability, reduces downtime, and enhances system resilience compared to traditional monolithic systems. The study contributes to the design of efficient, scalable, and fault-tolerant enterprise cloud applications
Article Information
Journal |
International Journal of Emerging Trends in Engineering and Management Research |
|---|---|
Volume (Issue) |
Vol. 4 No. 5 (2019): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) |
DOI |
|
Pages |
6142-6149 |
Published |
September 9, 2019 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Oluwasola Dada (%2019). Cloud Native Microservices Architecture for Scalable and Resilient Enterprise Application Deployment. International Journal of Emerging Trends in Engineering and Management Research , Vol. 4 No. 5 (2019): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 6142-6149. https://doi.org/10.15662/ijetemr.2019.0405001 |
References
2. Microservices Patterns Richardson, C. (2018). Microservices patterns: With examples in Java. Manning Publications.
3. Martin Fowler, M., & James Lewis (2014). Microservices: A definition of this new architectural term. ThoughtWorks.
4. National Institute of Standards and Technology. (2011). The NIST definition of cloud computing (Special Publication 800-145). U.S. Department of Commerce.
5. Brendan Burns, Joe Beda, & Kelsey Hightower. (2017). Kubernetes: Up and running: Dive into the future of infrastructure. O’Reilly Media.
6. Docker Inc.. (2017). Docker overview. Docker Documentation.
7. Len Bass, Ingo Weber, & Liming Zhu. (2015). DevOps: A software architect’s perspective. Addison-Wesley Professional.
8. Eberhard Wolff. (2016). Microservices: Flexible software architecture. Addison-Wesley Professional.
9. Red Hat. (2018). Introduction to microservices. Red Hat Documentation.
10. Nginx Inc.. (2016). Microservices reference architecture at NGINX. NGINX White Paper.
11. Mathew, A., & Asari, V. K. (2014, March). Rotation-invariant histogram features for threat object detection on pipeline right-of-way. In Video Surveillance and Transportation Imaging Applications 2014 (Vol. 9026, pp. 19-26). SPIE.
12. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2015). Design a Weight Based Sorting Distortion Algorithm for Privacy Preserving Data Mining. Middle-East Journal of Scientific Research, 23(3), 405-412.
13. Karthika, V., Brijet, Z., & Bharathi, N. (2012). Design of optimal controller for fluid catalytic cracking unit. Procedia Engineering, 38, 1150-1160.
14. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
15. Sugumar, R. B., & Anandharaj, K. (2016). Assessment of Bacterial Load in the Fresh Water Lake System of Tamil Nadu. Interl J Curr Microbiol App Sci, 5(6), 236-246.
16. Rengarajan, R. S. A. (2016). Secure verification technique for defending IP spoofing attacks.
17. Mathew, A. R., & Al Hajj, A. (2017). Secure communications on IoT and big data. Indian Journal of Science and Technology, 10(11).
18. Alex, A. T., Asari, V. K., & Mathew, A. (2012, October). Gradient feature matching for expression invariant face recognition using single reference image. In 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 851-856). IEEE.
19. Sasidevi, J., Sugumar, R., & Priya, P. S. (2015). New-Multi-Phase Distribution Network Intrusion Detection. International Journal, 5(3).
20. Mathew, A., & Asari, V. K. (2013, March). Tracking small targets in wide area motion imagery data. In Video Surveillance and Transportation Imaging Applications (Vol. 8663, pp. 69-78). SPIE.
21. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
22. Alex, A. T., Asari, V. K., & Mathew, A. (2013, March). Gradient feature matching for in-plane rotation invariant face sketch recognition. In Image Processing: Machine Vision Applications VI (Vol. 8661, pp. 46-58). SPIE.
23. Natarajan, R., & Sugumar, R. (2014). A survey on attacks in web usage mining. International Journal of Innovative Research in Computer and Communication Engineering, 2(5), 4470-5.
24. Satyanarayana, D., Mathew, A. R., & Sathyashree, S. (2016). An Architecture for Wireless Communication Systems using Li-Fi technology. In 8th International Conference on Latest Trends in Engineering and Technology (ICLTET’2016) (pp. 37-41).
25. Mathew, A. R. (2017). Cloud Technology and the Challenges for Forensics Investigators. DEStech Transactions on Computer Science and Engineering.
26. Begum, R. S., & Sugumar, R. (2015). A Conceptual Comparison of Artificial Bee Colony and Particle Swarm Optimization.
27. Soundappan, S. J., & Sugumar, R. (2016). Optimal knowledge extraction technique based on hybridisation of improved artificial bee colony algorithm and cuckoo search algorithm. International Journal of Business Intelligence and Data Mining, 11(4), 338-356.
28. Singh, T. J., & Sugumar, R. (2012, December). An extensibility of THEMIS billing system for the cloud computing environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-6). IEEE.
29. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2018). Trust based authentication technique for cluster based vehicular ad hoc networks (VANET). Wireless Networks, 24(2), 373-382.
30. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
31. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
32. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
33. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
34. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
35. Brijet, Z., Kumar, B. S., & Bharathi, N. (2017). Vehicle anti-theft system using fingerprint recognition technique. Open Academic Journal of Advanced Science and Technology, 1(1), 36-41.
36. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926