Containerization in Modern Software Architectures: Optimizing Scalability, Portability, and Resource Efficiency
DOI:
https://doi.org/10.36676/jrps.v16.i2.262Keywords:
Containerization, Contemporary Software Architectures, Scalability, Portability, Resource Optimization, Cloud Computing, Microservices, Kubernetes, Orchestration, Cloud-Native Technologies, Hybrid Architectures, Fault Tolerance, Load Balancing, Optimization Techniques.Abstract
Containerization has become a fundamental methodology in modern software architectures, transforming the paradigms of application development, deployment, and scaling. By encapsulating the applications and their dependencies in light, portable containers, organizations can attain a higher level of scalability, portability, and resource utilization. In this research, the development of containerization technologies with a special focus on their role in simplifying current software architectures is explored. While a detailed exploration of containerization in current literature is undertaken, there exists a notable research gap in its integration into large-scale systems, with special focus on the management of resources, fault tolerance, and orchestration in highly dynamic systems.
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• Ghosh, S., & Soni, M. (2024). AI-driven real-time performance optimization and comparison of virtual machines and containers in cloud environments. ResearchGate. https://doi.org/10.13140/RG.2.2.25599.23204ResearchGate
• Soni, M., & Ghosh, S. (2024). AI-driven cloud resource management and orchestration. International Journal of Innovative Research in Science, Engineering and Technology, 13(11), 206–213. https://doi.org/10.15680/IJIRSET.2024.1311019IJIRSET
• Gawande, S., & Gorthi, A. (2024). Containerization and Kubernetes: Scalable and efficient cloud-native applications. International Journal of Innovative Research in Science, Engineering and Technology, 13(11), 314–320. https://doi.org/10.15680/IJIRSET.2024.1311019ResearchGate
• Medel, V., Tolosana-Calasanz, R., Bañares, J. Á., Arronategui, U., & Rana, O. F. (2024). Characterising resource management performance in Kubernetes. arXiv. https://doi.org/10.48550/arXiv.2401.17125arXiv
• Waseem, M., Ahmad, A., Liang, P., Akbar, M. A., Khan, A. A., Ahmad, I., Setälä, M., & Mikkonen, T. (2024). Containerization in multi-cloud environment: Roles, strategies, challenges, and solutions for effective implementation. arXiv. https://doi.org/10.48550/arXiv.2403.12980arXiv+2arXiv+2arXiv+2
• Zhong, Z., Xu, M., Rodriguez, M. A., Xu, C., & Buyya, R. (2021). Machine learning-based orchestration of containers: A taxonomy and future irections. arXiv. https://doi.org/10.48550/arXiv.2106.12739arXiv+1arXiv+1
• Soni, M., & Ghosh, S. (2024). AI-driven self-healing container orchestration framework for energy-efficient and fault-tolerant Kubernetes clusters. Emerging Science Journal, 8(4), 31–45. https://doi.org/10.28991/esj-2024-04-31emergingpub.com
• Zhong, Z., Xu, M., Rodriguez, M. A., Xu, C., & Buyya, R. (2021). Machine learning-based orchestration of containers: A taxonomy and future directions. arXiv. https://doi.org/10.48550/arXiv.2106.12739
• Rodriguez, M. A., & Buyya, R. (2018). Container-based cluster orchestration systems: A taxonomy and future directions. arXiv. https://doi.org/10.48550/arXiv.1807.06193arXiv
• Soni, M., & Ghosh, S. (2024). Comparative analysis of container orchestration platforms: Kubernetes vs. Docker Swarm. International Journal of Scientific Research in Advanced Engineering, 11(5), 526–543. https://doi.org/10.13140/RG.2.2.25599.23204ResearchGate
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