Kubernetes Adoption for Legacy Rigid AWS Applications
Keywords:
Kubernetes, AWS, Agility, IT TransformationAbstract
Although it might be difficult for us to by moving outdated rigid apps to more contemporary designs like microservices is necessary to maintain competitiveness. An open-source container orchestration technology called Kubernetes provides a useful way to scale, manage, & deploying applications, especially in AWS settings. Kubernetes allows microservices to be incrementally modernized and existed apps to be containerized without a full rewrite. In addition to discussing typical issues & recommended practices for utilizing Kubernetes with AWS services, this paper examines many methods for containerizing, deploying & also scaling rigids. Examples from the real world show how companies may modernize gradually while maintaining stability & guaranteeing scalability & flexibility. The paper offers guidance for businesses looking to integrate Kubernetes & their update apps without interfering with business operations, emphasizing small, controllable changes through by planning, testing & iterative deployment.
References
1. Nosyk, Y. (2018). Migration of a legacy web application to the cloud.
2. Santos, T. C. (2018). Adopting Microservices (Doctoral dissertation, Tese de mestrado. Instituto Superior Técnico).
3. Laszewski, T., Arora, K., Farr, E., & Zonooz, P. (2018). Cloud Native Architectures: Design high-availability and cost-effective applications for the cloud. Packt Publishing Ltd.
4. Pérez, A., Moltó, G., Caballer, M., & Calatrava, A. (2018). Serverless computing for container-based architectures. Future Generation Computer Systems, 83, 50-59.
5. Singh, S., & Singh, N. (2016, July). Containers & Docker: Emerging roles & future of Cloud technology. In 2016 2nd international conference on applied and theoretical computing and communication technology (iCATccT) (pp. 804-807). IEEE.
6. Ravula, S. (2017). Achieving Continuous Delivery of Immutable Containerized Microservices with Mesos/Marathon (Master's thesis).
7. Luksa, M. (2017). Kubernetes in action. Simon and Schuster.
8. Trivedi, H., & Kulkarni, A. (2018). Hands-On Serverless Applications with Kotlin: Develop scalable and cost-effective web applications using AWS Lambda and Kotlin. Packt Publishing Ltd.
9. 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.
10. Kratzke, N., & Peinl, R. (2016, September). Clouns-a cloud-native application reference model for enterprise architects. In 2016 IEEE 20th International Enterprise Distributed Object Computing Workshop (EDOCW) (pp. 1-10). IEEE.
11. Ranganathan, R. (2018). A highly-available and scalable microservice architecture for access management (Master's thesis).
12. Mahajan, A., Gupta, M. K., & Sundar, S. (2018). Cloud-Native Applications in Java: Build microservice-based cloud-native applications that dynamically scale. Packt Publishing Ltd.
13. Larrucea, X., Santamaria, I., Colomo-Palacios, R., & Ebert, C. (2018). Microservices. IEEE Software, 35(3), 96-100.
14. Ivanov, P. (2016). Continuous Integration and Continuous Deployment Practices: Studying practices and techniques for implementing continuous integration and continuous deployment pipelines in software projects. Distributed Learning and Broad Applications in Scientific Research, 2, 1-9.
15. Jamshidi, P., Pahl, C., Mendonça, N. C., Lewis, J., & Tilkov, S. (2018). Microservices: The journey so far and challenges ahead. IEEE Software, 35(3), 24-35.
16. Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.
17. Gade, K. R. (2017). Integrations: ETL/ELT, Data Integration Challenges, Integration Patterns. Innovative Computer Sciences Journal, 3(1).
18. Gade, K. R. (2017). Migrations: Challenges and Best Practices for Migrating Legacy Systems to Cloud-Based Platforms. Innovative Computer Sciences Journal, 3(1).
19. Naresh Dulam. Apache Spark: The Future Beyond MapReduce. Distributed Learning and Broad Applications in Scientific Research, vol. 1, Dec. 2015, pp. 136-5
20. Naresh Dulam. DataOps: Streamlining Data Management for Big Data and Analytics . Distributed Learning and Broad Applications in Scientific Research, vol. 2, Oct. 2016, pp. 28-50
21. Naresh Dulam, et al. Kubernetes Gains Traction: Orchestrating Data Workloads. Distributed Learning and Broad Applications in Scientific Research, vol. 3, May 2017, pp. 69-93
22. Naresh Dulam, et al. Snowflake Vs Redshift: Which Cloud Data Warehouse Is Right for You? . Distributed Learning and Broad Applications in Scientific Research, vol. 4, Oct. 2018, pp. 221-40
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
