Cloud-Native Java Applications on AWS ECS and Kubernetes

  • Authors

    • Venkatesh Satla Full Stack Lead Java Developer at Innosoul, Inc, USA. Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-2023PII6T2W

    Published 08-08-2023

  • Cloud-Native Applications, Java Microservices, AWS ECS, Kubernetes, Docker, Container Orchestration, Devops, CI/CD, Scalability, Cloud Computing

    Issue

    Vol. 6 No. 2 (2023)

    Section

    Articles

  • Abstract

    The paradigm of cloud-native application development has changed the concept of development of modern software systems, taking advantage of the scalability, flexibility, and resilience capabilities provided by cloud computing platforms for creation, deployment, and management. Thanks to its platform freedom, stable ecosystem, security features and a big collection of frameworks to construct large-scale applications, Java remains one of the most popular programming languages in the enterprise world. Containerization technologies also make it easier to deploy Java programs so they execute safely in many diverse environments. Container orchestration systems like Amazon Elastic Container Service (AWS ECS) and Kubernetes are powerful tools that help manage containerized workloads and automate deployment, scaling, networking, and high availability. The paper discusses the design and implementation of cloud native Java apps on AWS ECS and Kubernetes, including architectural, operational and performance issues. The purpose of this study endeavor is to evaluate several platforms in terms of their deployment performance, scalability, resource utilization, management complexity and applicability for enterprise applications. The methodology employed is based on qualitative and comparative approach by literature review, architectural analysis and real implementation scenarios. The results demonstrate that AWS ECS is user-friendly and provides an easy interface to AWS whereas Kubernetes has more flexibility, portability and ecosystem support. The study provides practical insights for enterprises looking for the best orchestration solutions for their cloud-native Java application deployment and modernization strategy.

  • References

    [1] Mahajan, A., & Gupta, M. K. (2018). Cloud-Native Applications in Java: Build microservice-based cloud-native applications that dynamically scale. Packt Publishing Ltd.

    [2] Kratzke, N., & Peinl, R. (2016, September). Clouds-a cloud-native application reference model for enterprise architects. In 2016 IEEE 20th International Enterprise Distributed Object Computing Workshop (EDOCW) (pp. 1-10). IEEE.

    [3] Parakala, A. (2021). Building Analytics-Driven Bots: RPA Meets Business Intelligence. International Journal of Emerging Research in Engineering and Technology, 2(1), 77-87. https://doi.org/10.63282/3050-922X.IJERET-V2I1P109

    [4] Marchioni, F. (2019). Hands-on Cloud-native Applications with Java and Quarkus: Build High Performance, Kubernetes-native Java Serverless Applications. Packt Publishing Ltd.

    [5] Srigadde, B. R. (2023). The Hidden Gem: Lightning Headless Component. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 244-254. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P125

    [6] Kertész, D. R., Farkas, K., & Szabó, G. (2021). Best Practices of Cloud Native Application Development. Bachelor of profession’s thesis, Budapest University of Technology and Economics, Budapest.

    [7] Vppalapati, M., & Talasila, P. K. (2021). Failure without Faults: How Enterprise Storage Systems Degrade Long Before They Break. International Journal of Emerging Trends in Computer Science and Information Technology, 2(1), 115-123. https://doi.org/10.63282/3050-9246.IJETCSIT-V2I1P113

    [8] Kumar Doodala, A. N. (2023). Offline-First Android Architecture for waste management in low connectivity zones. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 201-209. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P121

    [9] Domingus, J., & Arundel, J. (2022). Cloud Native DevOps with Kubernetes. " O'Reilly Media, Inc.".

    [10] Gaddam, R. R. (2022). Cost-Aware Autoscaling for Batch vs. Online Inference. International Journal of Emerging Trends in Computer Science and Information Technology, 3(4), 134-143. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I4P113

    [11] Arundel, J., & Domingus, J. (2019). Cloud Native DevOps with Kubernetes: building, deploying, and scaling modern applications in the Cloud. O'Reilly Media.

    [12] Muppaneni, K. (2022). Comparative Analysis of Client-Side Storage Mechanisms. International Journal of AI, BigData, Computational and Management Studies, 3(1), 171-182. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I1P119

    [13] Vitale, T. (2022). Cloud Native Spring in Action: With Spring Boot and Kubernetes. Simon and Schuster.

    [14] Allenki, S. S., & Korutla, R. (2021). Agile Development in Practice: From Intern to Contributor. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(3), 91-103. https://doi.org/10.63282/3050-9262.IJAIDSML-V2I3P110

    [15] Vppalapati, M., & Talasila, P. K. (2021). Failure without Faults: How Enterprise Storage Systems Degrade Long Before They Break. International Journal of Emerging Trends in Computer Science and Information Technology, 2(1), 115-123. https://doi.org/10.63282/3050-9246.IJETCSIT-V2I1P113

    [16] Fugaro, L., & Vocale, M. (2019). Hands-On Cloud-Native Microservices with Jakarta EE: Build Scalable and Reactive Microservices with Docker, Kubernetes, and OpenShift. Packt Publishing Ltd.

    [17] Muppaneni, R. K. (2022). Data Privacy in the Age of AI: How Dynamics 365 Handles Regulatory Challenges. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(4), 159-170. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I4P117

    [18] Vanapalli, Satyendra Kumar. "Bridging Business and Technology: The Role of CRM in Digital Transformation." International Journal of Artificial Intelligence & Digital Transformation 5.2 (2022): 01-17.

    [19] Sartoni, M. (2022). AWS Services for Cloud Robotics Applications (Doctoral dissertation, Politecnico di Torino).

    [20] Gaddam, R. R. (2021). Vertex AI as a Unified Control Plane for MLOps. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(2), 92-102. https://doi.org/10.63282/3050-9262.IJAIDSML-V2I2P110

    [21] Shiramalla, R. (2022). Design of a Unified API Interface Using Workato for Cross-Platform Data Orchestration Between Salesforce and Oracle ERP. International Journal of Emerging Trends in Computer Science and Information Technology, 3(1), 157-168. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I1P118

    [22] Kommuru, Madhurima. "Concurrency is hard: Java vs Python pitfalls you can't ignore." International Journal of Modern Research in Science & Engineering 5.2 (2022): 01-18.

    [23] Carpenter, J., & McFadin, P. (2022). Managing cloud native data on Kubernetes: architecting cloud native data services using open source technology. " O'Reilly Media, Inc.".

    [24] Muppaneni, R. K. (2020). Retail Reimagined: How Dynamics 365 Commerce Is Driving Omnichannel Experiences. International Journal of AI, BigData, Computational and Management Studies, 1(1), 49-59. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V1I1P106

    [25] Muppaneni, R. K. (2022). Data Privacy in the Age of AI: How Dynamics 365 Handles Regulatory Challenges. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(4), 159-170. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I4P117

    [26] Vanapalli, Satyendra Kumar. "AI-Driven Fraud Detection in Financial Systems: A CRM-Centric Approach." International Journal of Machine Learning and Predictive Analytics 5.2 (2022): 01-14.

    [27] Sethi, M. (2017). Cloud Native Python. Packt Publishing Ltd.

    [28] Katangoori, S., & Katangoori, A. (2021). AI-Augmented Data Governance: Enabling Intelligent Access, Lineage, and Compliance across Hybrid Clouds. American International Journal of Computer Science and Technology, 3(6), 36-45. https://doi.org/10.63282/3117-5481/AIJCST-V3I6P104

    [29] Vppalapati, M. (2022). The Storage Stack Nobody Draws: Cabling, Panels, and the Illusion of Isolation. International Journal of Emerging Research in Engineering and Technology, 3(2), 211-220. https://doi.org/10.63282/3050-922X.IJERET-V3I2P121

    [30] Suryadevara, S. S. K. (2022). Knowledge-Graph-Enabled Tagging and Taxonomy Automation Framework. American International Journal of Computer Science and Technology, 4(1), 77-89. https://doi.org/10.63282/3117-5481/AIJCST-V4I1P108

    [31] Saleh, A., & Karslioglu, M. (2021). Kubernetes in Production Best Practices: Build and manage highly available production-ready Kubernetes clusters. Packt Publishing Ltd.

    [32] Allenki, S. S. (2022). Securing Databases in the Cloud with RBAC and Encryption Best Practices. International Journal of Emerging Research in Engineering and Technology, 3(3), 173-182. https://doi.org/10.63282/3050-922X.IJERET-V3I3P117

    [33] Kumar Doodala, A. N., & Thatraju, S. (2022). NLP-Driven Benefits Interpretation Engine for Personalized Member Communication. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(1), 173-183. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I1P118

    [34] Saeed, L., & Abdallah, G. (2022). Pro Cloud Native Java EE Apps.

    [35] Muppaneni, K. (2021). HTTP/3 & REST Latency Improvement. International Journal of Emerging Research in Engineering and Technology, 2(1), 122-132. https://doi.org/10.63282/3050-922X.IJERET-V2I1P113

    [36] Suryadevara, S. S. K., & Polinati, A. K. (2022). Cross-Cloud Governance Engine Using Policy-as-Code for CMS Platforms. International Journal of Emerging Research in Engineering and Technology, 3(4), 165-175. https://doi.org/10.63282/3050-922X.IJERET-V3I4P118

    [37] Srigadde, B. R. (2020). When Force Is With You but Not Lightning Component. American International Journal of Computer Science and Technology, 2(1), 23-33. https://doi.org/10.63282/3117-5481/AIJCST-V2I1P103

    [38] Khatri, A., & Khatri, V. (2020). Mastering Service Mesh: Enhance, secure, and observe cloud-native applications with Istio, Linkerd, and Consul. Packt Publishing Ltd.

    [39] Katangoori, S., & Deore, S. (2022). Edge-Cloud Hybrid Data Pipelines: Architectures for Federated Analytics and Learning. American International Journal of Computer Science and Technology, 4(3), 20-34. https://doi.org/10.63282/3117-5481/AIJCST-V4I3P103

    [40] Parakala, A. (2022). Integrating Salesforce and UiPath: Cross-System Intelligent Automation. International Journal of Emerging Trends in Computer Science and Information Technology, 3(4), 88-99. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I4P109

    [41] Leymann, F., Breitenbücher, U., Wagner, S., & Wettinger, J. (2016, April). Native cloud applications: why monolithic virtualization is not their foundation. In International Conference on Cloud Computing and Services Science (pp. 16-40). Cham: Springer International Publishing.

  • Downloads