Cloud-Native Software Design: Expandable and Durable Engineering Principles

Authors

  • Sandeep Chinamanagonda Senior Software Engineer at Oracle Cloud infrastructure, USA Author
  • Hitesh Allam Software Engineer at Concor IT, USA Author
  • Jayaram Immaneni SRE Lead at JP Morgan Chase, USA Author

Keywords:

Cloud-native design, scalable software, reliable engineering, containerization, DevOps practices

Abstract

To fully benefits from cloud computing, cloud-native software designs must be strategically prioritizing scalability, dependability & efficiencies. Businesses can swiftly adjust, manage demands surges & they ensure high availability with the cloud-native apps. Continuous delivery, automations, containerization & the microservices are very important tenets of cloud-native engineering. Applications may be divided into small-scale, standalone services to facilitate safer positions & quicker iterations. Scaling & deployment are made very easier by containers, which can be provide consistency & portability across environments. Delivery cycles are accelerated more, dependability is increased & human labour is decreases by automation using solutions like CI/CD pipelines & infrastructure as a code. Observability, monitoring & resilience engineering are essential for developing systems that can recover from failures, self-heal & provide the actual time performance insights. Multi-region positions, load balancing & automated scaling all contributed to increased reliability. Using DevSecOps techniques are security is included into developments at every stage of guaranteeing systems security without impeding innovations. With the help of cloud-native designs, businesses can create software that is durable, scalable & responsive to the demands of contemporary users. Businesses may increase productivity, maintain their competitiveness & adjust to changing consumer needs by adopting these ideas.

References

1. Katari, A., & Vangala, R. Data Privacy and Compliance in Cloud Data Management for Fintech.

2. Davis, C. (2019). Cloud Native Patterns: Designing Change-Tolerant Software. Simonand Schuster.

3. Laszewski, T., Arora, K., Farr, E., & Zonooz, P. (2018). Cloud Native Architectures: Design high-availability and cost-effective applications for the cloud. Packet Publishing Ltd.

4. Katari, A., Ankam, M., & Shankar, R. Data Versioning and Time Travel In Delta Lake for Financial Services: Use Cases and Implementation.

5. Toffetti, G., Brunner, S., Blöchlinger, M., Spillner, J., & Bohnert, T. M. (2017). Self-managing cloud-native applications: Design, implementation, and experience. Future Generation Computer Systems, 72, 165-179.

6. Indrasiri, K., & Suhothayan, S. (2021). Design Patterns for Cloud Native Applications. " O'Reilly Media, Inc.".

7. Henning, S., & Hasselbring, W. (2022). A configurable method for benchmarking scalability of cloud-native applications. Empirical Software Engineering, 27(6), 143.

8. Garrison, J., & Nova, K. (2017). Cloud native infrastructure: Patterns for scalable infrastructure and applications in a dynamic environment. " O'Reilly Media, Inc.".

9. Dutt, D. G. (2019). Cloud native data center networking: architecture, protocols, and tools. O'Reilly Media.

10. Winn, D. C. (2016). Cloud Foundry: the cloud-native platform. " O'Reilly Media, Inc.".

11. Brunner, S., Blöchlinger, M., Toffetti, G., Spillner, J., & Bohnert, T. M. (2015, December). Experimental evaluation of the cloud-native application design. In 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC) (pp. 488-493). IEEE.

12. Katari, A. (2022). Performance Optimization in Delta Lake for Financial Data: Techniques and Best Practices. MZ Computing Journal, 3(2).

13. Imadali, S., & Bousselmi, A. (2018, November). Cloud native 5g virtual network functions: Design principles and use cases. In 2018 IEEE 8th International Symposium on Cloud and Service Computing (SC2) (pp. 91-96). IEEE.

14. Olabanji, D. O. (2022). Towards the development of a decision framework for portability in cloud-native architecture deployment (Doctoral dissertation, University of Portsmouth).

15. Raghunathan, S. (2021). Resilience by Design: A Comprehensive Guide to Enhancing Resilience through Cloud Native Chaos Engineering. Journal of Scientific and Engineering Research, 8(8), 181-185.

16. Katari, A. (2019). Real-Time Data Replication in Fintech: Technologies and Best Practices. Innovative Computer Sciences Journal, 5(1).

17. Katari, A. (2019). ETL for Real-Time Financial Analytics: Architectures and Challenges. Innovative Computer Sciences Journal, 5(1).

18. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2022). The Shift Towards Distributed Data Architectures in Cloud Environments. Innovative Computer Sciences Journal, 8(1).

19. Nookala, G. (2022). Improving Business Intelligence through Agile Data Modeling: A Case Study. Journal of Computational Innovation, 2(1).

20. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2021). Unified Data Architectures: Blending Data Lake, Data Warehouse, and Data Mart Architectures. MZ Computing Journal, 2(2).

21. Nookala, G. (2021). Automated Data Warehouse Optimization Using Machine Learning Algorithms. Journal of Computational Innovation, 1(1).

22. Boda, V. V. R., & Immaneni, J. (2022). Optimizing CI/CD in Healthcare: Tried and True Techniques. Innovative Computer Sciences Journal, 8(1).

23. Immaneni, J. (2022). End-to-End MLOps in Financial Services: Resilient Machine Learning with Kubernetes. Journal of Computational Innovation, 2(1).

24. Boda, V. V. R., & Immaneni, J. (2021). Healthcare in the Fast Lane: How Kubernetes and Microservices Are Making It Happen. Innovative Computer Sciences Journal, 7(1).

25. Immaneni, J. (2021). Using Swarm Intelligence and Graph Databases for Real-Time Fraud Detection. Journal of Computational Innovation, 1(1).

26. Thumburu, S. K. R. (2022). EDI and Blockchain in Supply Chain: A Security Analysis. Journal of Innovative Technologies, 5(1).

27. Thumburu, S. K. R. (2022). A Framework for Seamless EDI Migrations to the Cloud: Best Practices and Challenges. Innovative Engineering Sciences Journal, 2(1).

28. Thumburu, S. K. R. (2022). The Impact of Cloud Migration on EDI Costs and Performance. Innovative Engineering Sciences Journal, 2(1).

29. Thumburu, S. K. R. (2022). AI-Powered EDI Migration Tools: A Review. Innovative Computer Sciences Journal, 8(1).

30. Komandla, V. Enhancing Product Development through Continuous Feedback Integration “Vineela Komandla”.

31. Komandla, V. Enhancing Security and Growth: Evaluating Password Vault Solutions for Fintech Companies.

32. Komandla, V. Strategic Feature Prioritization: Maximizing Value through User-Centric Roadmaps.

33. Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.

34. Muneer Ahmed Salamkar, and Karthik Allam. Data Integration Techniques: Exploring Tools and Methodologies for Harmonizing Data across Diverse Systems and Sources. Distributed Learning and Broad Applications in Scientific Research, vol. 6, June 2020

35. Muneer Ahmed Salamkar, et al. The Big Data Ecosystem: An Overview of Critical Technologies Like Hadoop, Spark, and Their Roles in Data Processing Landscapes. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Sept. 2021, pp. 355-77

36. Muneer Ahmed Salamkar. Scalable Data Architectures: Key Principles for Building Systems That Efficiently Manage Growing Data Volumes and Complexity. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, Jan. 2021, pp. 251-70

37. Muneer Ahmed Salamkar, and Jayaram Immaneni. Automated Data Pipeline Creation: Leveraging ML Algorithms to Design and Optimize Data Pipelines. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, June 2021, pp. 230-5

38. Naresh Dulam, et al. “Data Mesh Best Practices: Governance, Domains, and Data Products”. Australian Journal of Machine Learning Research & Applications, vol. 2, no. 1, May 2022, pp. 524-47

39. Naresh Dulam, et al. “Apache Iceberg 1.0: The Future of Table Formats in Data Lakes”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 1, Feb. 2022, pp. 519-42

40. Naresh Dulam, et al. “Kubernetes at the Edge: Enabling AI and Big Data Workloads in Remote Locations”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, Oct. 2022, pp. 251-77

41. Naresh Dulam, et al. “Data Mesh and Data Governance: Finding the Balance”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, Dec. 2022, pp. 226-50

42. Sarbaree Mishra. “A Reinforcement Learning Approach for Training Complex Decision Making Models”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, July 2022, pp. 329-52

4.3 Sarbaree Mishra, et al. “Leveraging in-Memory Computing for Speeding up Apache Spark and Hadoop Distributed Data Processing”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, Sept. 2022, pp. 304-28

44. Sarbaree Mishra. “Comparing Apache Iceberg and Databricks in Building Data Lakes and Mesh Architectures”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, Nov. 2022, pp. 278-03

45. Sarbaree Mishra. “Reducing Points of Failure - a Hybrid and Multi-Cloud Deployment Strategy With Snowflake”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 1, Jan. 2022, pp. 568-95

46. Babulal Shaik. Network Isolation Techniques in Multi-Tenant EKS Clusters. Distributed Learning and Broad Applications in Scientific Research, vol. 6, July 2020

47. Babulal Shaik. Automating Compliance in Amazon EKS Clusters With Custom Policies . Journal of Artificial Intelligence Research and Applications, vol. 1, no. 1, Jan. 2021, pp. 587-10

Published

28-02-2023

How to Cite

Cloud-Native Software Design: Expandable and Durable Engineering Principles. (2023). Journal of Artificial Intelligence Research and Applications, 3(1), 904-924. https://jairajournal.org/index.php/publication/article/view/69