- Thursday, Oct 23rd, 2025
| TITLE | Hybrid Cloud Architectures for High-Performance AI Applications |
|---|---|
| ABSTRACT | The growing complexity and computational demands of Artificial Intelligence (AI) applications, particularly in fields such as natural language processing, computer vision, and autonomous systems, have driven the need for highly scalable and performance-optimized computing infrastructures. Hybrid cloud architectures have emerged as a compelling solution by combining the scalability and flexibility of public cloud platforms with the security and control of private clouds or on-premises infrastructure. This paper explores the application of hybrid cloud architectures to support high-performance AI workloads, focusing on the optimization of compute resources, data storage, and networking. We begin by outlining the core components of hybrid cloud models and their roles in accelerating AI model training and inference. Through a review of recent literature and case studies, we highlight various deployment strategies, including the use of container orchestration (e.g., Kubernetes), distributed training frameworks (e.g., Horovod, Ray), and GPU/TPU accelerators. The research methodology includes comparative performance testing across different hybrid setups, along with analysis of cost efficiency, latency, and data governance. Key findings suggest that hybrid cloud architectures can offer up to 40% performance improvement and 30% cost savings when workloads are intelligently partitioned between cloud and on-prem resources. However, challenges such as data synchronization, network bottlenecks, and security compliance remain significant. Our proposed workflow integrates CI/CD pipelines, autoscaling policies, and intelligent data sharding to streamline AI deployments across hybrid environments. This study provides practical insights and a reference architecture for organizations aiming to deploy scalable, high-performance AI systems in hybrid cloud settings. Future research directions include the integration of AIOps for self-managing infrastructure and the use of edge-cloud collaboration models. |
| AUTHOR | Manjiri Prabhu Dr. D. Y. Patil College of Engineering and Innovation, Pune, India |
| PUBLICATION DATE | 2025-09-17 |
| VOLUME | 12 |
| DOI | DOI:10.15680/IJARETY.2025.1204087 |
| 87_Hybrid Cloud Architectures for High-Performance AI Applications.pdf | |
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