Containerization and Kubernetes: Scalable and Efficient Cloud-Native Applications


Authors : Sachin Gawande; Anupam Gorthi

Volume/Issue : Volume 9 - 2024, Issue 11 - November


Google Scholar : https://tinyurl.com/2p99z682

Scribd : https://tinyurl.com/26z549rn

DOI : https://doi.org/10.38124/ijisrt/IJISRT24NOV314

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.


Abstract : The rise of cloud computing has revolutionized the way applications are developed, deployed, and managed. Containerization and Kubernetes have emerged as key technologies in this landscape, enabling organizations to build scalable, efficient, and portable cloud-native applications. This paper explores the fundamental concepts of containerization and Kubernetes, their benefits in cloud-native application development, and best practices for implementation. It also discusses advanced tools like Karpenter for optimizing cluster autoscaling. By leveraging these technologies, organizations can achieve greater flexibility, resource efficiency, and operational consistency across diverse cloud environments. The findings suggest that containerization, Kubernetes, and associated tools are critical enablers for modern application architectures, facilitating rapid development, seamless scaling, and efficient resource utilization in cloud-native ecosystems.

Keywords : Containerization, Kubernetes, Karpenter, Cloud- Native, Microservices, DevOps, Orchestration, Scalability, Portability.

References :

  1. Burns, B., Grant, B., Oppenheimer, D., Brewer, E., & Wilkes, J. (2016). Borg, Omega, and Kubernetes. Communications of the ACM, 59(5), 50-57.
  2. Bernstein, D. (2014). Containers and cloud: From LXC to Docker to Kubernetes. IEEE Cloud Computing, 1(3), 81-84.
  3. Pahl, C., Brogi, A., Soldani, J., & Jamshidi, P. (2019). Cloud container technologies: a state-of-the-art review. IEEE Transactions on Cloud Computing, 7(3), 677-692.
  4. Fink, J. (2014). Docker: a software as a service, operating system-level virtualization framework. Code4Lib Journal, 25.
  5. Morabito, R., Kjällman, J., & Komu, M. (2015). Hypervisors vs. lightweight virtualization: a performance comparison. In 2015 IEEE International Conference on Cloud Engineering (pp. 386-393). IEEE.
  6. Combe, T., Martin, A., & Di Pietro, R. (2016). To Docker or not to Docker: A security perspective. IEEE Cloud Computing, 3(5), 54-62.
  7. Casalicchio, E., & Perciballi, V. (2017). Auto-scaling of containers: The impact of relative and absolute metrics. In 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS* W) (pp. 207-214). IEEE.
  8. Kubernetes. (2021). Production-Grade Container Orchestration. Retrieved from https://kubernetes.io/
  9. Medel, V., Tolosana-Calasanz, R., Bañares, J. Á., Arronategui, U., & Rana, O. F. (2018). Characterising resource management performance in Kubernetes. Computers & Electrical Engineering, 68, 286-297.
  10. Kratzke, N., & Quint, P. C. (2017). Understanding cloud-native applications after 10 years of cloud computing-a systematic mapping study. Journal of Systems and Software, 126, 1-16.
  11. Xu, C., Rajamani, K., & Felter, W. (2017). NBWGuard: Realizing network QoS for Kubernetes. In Proceedings of the Workshop on Hot Topics in Container Networking and Networked Systems (pp. 43-48).
  12. Vaquero, L. M., Rodero-Merino, L., & Buyya, R. (2011). Dynamically scaling applications in the cloud. ACM SIGCOMM Computer Communication Review, 41(1), 45-52.
  13. Shu, R., Gu, X., & Enck, W. (2017). A study of security vulnerabilities on docker hub. In Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy (pp. 269-280).
  14. AWS. (2021). Karpenter. Retrieved from https://github.com/aws/karpenter
  15. Kilcioglu, C., Rao, J. R., Kannan, A., & McAfee, L. P. (2017). Chaos monkey: Adaptive resource provisioning for cloud-based services. In Proceedings of the 2017 IEEE International Conference on Big Data (Big Data) (pp. 2640-2649). IEEE.
  16. Baresi, L., Guinea, S., Leva, A., & Quattrocchi, G. (2016). A discrete-time feedback controller for containerized cloud applications. In Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering (pp. 217-228).
  17. Qu, C., Calheiros, R. N., & Buyya, R. (2018). Auto-scaling web applications in clouds: A taxonomy and survey. ACM Computing Surveys (CSUR), 51(4), 1-33.
  18. Casalicchio, E. (2019). Container orchestration: A survey. In Systems Modeling: Methodologies and Tools (pp. 221-235). Springer, Cham.
  19. Jaramillo, D., Nguyen, D. V., & Smart, R. (2016). Leveraging microservices architecture by using Docker technology. In SoutheastCon 2016 (pp. 1-5). IEEE.
  20. Al-Dhuraibi, Y., Paraiso, F., Djarallah, N., & Merle, P. (2017). Elasticity in cloud computing: state of the art and research challenges. IEEE Transactions on Services Computing, 11(2), 430-447.
  21. Pahl, C., & Lee, B. (2015). Containers and cluster orchestration for IaaS clouds. IEEE Cloud Computing, 2(5), 68-75.
  22. Zhao, Y., Li, K., Wang, X., & Xu, C. (2019). Kubernetes-based dynamic resource management for multi-tenant microservice environments. Concurrency and Computation: Practice and Experience, 31(18), e5114.
  23. Balalaie, A., Heydarnoori, A., & Jamshidi, P. (2016). Microservices architecture enables DevOps: Migration to a cloud-native architecture. IEEE Software, 33(3), 42-52.
  24. Newman, S. (2015). Building microservices: designing fine-grained systems. O'Reilly Media, Inc.
  25. Shahin, M., Babar, M. A., & Zhu, L. (2017). Continuous integration, delivery and deployment: a systematic review on approaches, tools, challenges and practices. IEEE Access, 5, 3909-3943.
  26. Morris, K. (2016). Infrastructure as code: managing servers in the cloud. O'Reilly Media, Inc.
  27. Karatas, F., Bourimi, M., Kesdogan, D., Villanueva, F. J., & Faber, A. (2020). Towards secure and scalable cloud-native architectures for cyber-physical systems. Sensors, 20(11), 3092.
  28. Souppaya, M., Morello, J., & Scarfone, K. (2017). Application container security guide. NIST Special Publication, 800, 190.
  29. Zheng, C., & Thain, D. (2015). Integrating containers into workflows: A case study using makeflow, work queue, and docker. In Proceedings of the 8th International Workshop on Virtualization Technologies in Distributed Computing (pp. 31-38).

The rise of cloud computing has revolutionized the way applications are developed, deployed, and managed. Containerization and Kubernetes have emerged as key technologies in this landscape, enabling organizations to build scalable, efficient, and portable cloud-native applications. This paper explores the fundamental concepts of containerization and Kubernetes, their benefits in cloud-native application development, and best practices for implementation. It also discusses advanced tools like Karpenter for optimizing cluster autoscaling. By leveraging these technologies, organizations can achieve greater flexibility, resource efficiency, and operational consistency across diverse cloud environments. The findings suggest that containerization, Kubernetes, and associated tools are critical enablers for modern application architectures, facilitating rapid development, seamless scaling, and efficient resource utilization in cloud-native ecosystems.

Keywords : Containerization, Kubernetes, Karpenter, Cloud- Native, Microservices, DevOps, Orchestration, Scalability, Portability.

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe