Authors: Artur Eduardovich Karapetyan, Lusine Rafikovna Minasyan, Hovhannes Grigorievich Manukyan, Ani Serobovna Avetisyan, Vardan Levonovich Sahakyan
Abstract: The exponential growth of genomic data presents immense challenges in terms of storage, processing, and analytics. Hybrid cloud systems—combining on-premises resources with scalable cloud services—offer a compelling solution for addressing these computational demands. This paper presents a unified architectural model designed to optimize genomic data analytics in hybrid cloud environments. By integrating containerized bioinformatics workflows, secure data orchestration mechanisms, and AI-driven scheduling, the proposed framework ensures agility, scalability, and compliance. We explore the role of cloud bursting for peak genomic analysis workloads, address data residency and regulatory concerns, and demonstrate performance improvements across typical use cases such as variant calling and gene expression analysis. This architecture supports real-time analytics, secure collaboration, and cross-institutional data sharing in the genomics domain.
