Hybrid Cloud Integration of Generative AI Models in Microservice Architectures with Java and Kubernetes
Keywords:
Hybrid Cloud, Generative AI, Microservices, Kubernetes, Java, Distributed Systems, MLOpsAbstract
The rapid evolution of generative AI technologies necessitates scalable, resilient, and maintainable deployment environments, particularly for enterprise applications. This paper explores the hybrid cloud integration of generative AI models within microservice architectures, focusing on implementation using Java-based services orchestrated by Kubernetes. We present an architecture that enables seamless AI service deployment across public and private clouds, ensuring performance, fault tolerance, and data security. Through architectural analysis and illustrative case studies, we demonstrate the feasibility of containerized AI services leveraging modern DevOps pipelines. The paper concludes with discussions on observed performance trends, key integration challenges, and future directions for adaptive AI-cloud orchestration.
References
Dragoni, N., et al. (2015). Microservices: Yesterday, Today, and Tomorrow. Present and Ulterior Software Engineering, Springer.
Devalla, S. (2020). Performance benchmarking of Java garbage collectors in containerized microservices. Journal of Scientific and Engineering Research, 7(6), 326–334.
Pahl, C., & Jamshidi, P. (2016). Microservices: A Systematic Mapping Study. CLOSER 2016, 137–146.
Devalla, S. (2020). Beyond Redux: State management and developer productivity in enterprise SPAs. European Journal of Advances in Engineering and Technology, 7(4), 70–78.
Dean, J., et al. (2012). Large Scale Distributed Deep Networks. Advances in Neural Information Processing Systems, 25.
Devalla, S. (2018). Performance benchmarking of RESTful and SOAP APIs in enterprise IoT control systems. Journal of Scientific and Engineering Research, 5(11), 376–390.
Amatriain, X., & Basilico, J. (2015). Netflix Recommendations: Beyond the 5 stars. The Netflix Tech Blog.
Bernstein, D. (2014). Containers and Cloud: From LXC to Docker to Kubernetes. IEEE Cloud Computing, 1(3), 81–84.
Devalla, S. (2019). Unveiling the enterprise value of PaaS: A comparative study of productivity, scalability, and cost efficiency against SaaS and IaaS. European Journal of Advances in Engineering and Technology, 6(2), 120–126.
Zaharia, M., et al. (2010). Spark: Cluster Computing with Working Sets. USENIX Conference on Hot Topics in Cloud Computing.
Devalla, S. (2019). Adaptive security frameworks for Java EE 8 and JSF: Automating threat detection and mitigation in enterprise web applications. Journal of Scientific and Engineering Research, 6(10), 326–334.
