Agentic AI-enabled Fog-to-Cloud Approach for Collaborative and Accessible Genomic Data Analysis

Next-generation sequencing technologies generate massive volumes of genomic data, placing significant demands on computational infrastructure for storage, processing, and analysis. While Cloud computing provides powerful capabilities for managing large-scale datasets, its centralized architecture introduces critical limitations, particularly in terms of bandwidth constraints, latency, and data transfer overhead. To address these challenges, computational resources must be extended closer to data sources. In this context, Fog computing emerges as a complementary paradigm that enables distributed, low-latency processing and context-aware data management at the network edge. This paper proposes a resource management architecture based on a virtualized Collaborative Fog-to-Cloud Computing (CoF2C) platform that seamlessly integrates the Cloud and Fog paradigms to efficiently handle large-scale genomic data. The architecture incorporates Genomic Testing Users (GTUs) at the IoT layer and their corresponding virtual representations at the Fog layer, forming a cyber-physical overlay network that enhances system coordination and data accessibility. Furthermore, the integration of autonomous Agentic Artificial Intelligence (AAI) enables dynamic workload orchestration, intelligent routing, and real-time adaptation of next-generation sequencing (NGS) workflows. The proposed CoF2C model improves system performance, scalability, and energy efficiency while significantly reducing latency and response times. By enabling localized processing and fostering collaborative data sharing across distributed environments, the architecture supports more responsive, robust, and context-aware genomic analysis. These capabilities position CoF2C as a promising solution for advancing next-generation genomics research, precision medicine, and large-scale biomedical collaborations.