Novel hybrid edge-cloud framework for efficient and sustainable omics data management

Introduction: The healthcare landscape is rapidly evolving through the integration of diverse data sources such as electronic health records, omics, and genomic data into patient profiles, enhancing personalized medicine and system interoperability. However, this transformation faces challenges in data integration and analysis, compounded by technologic advancements and the increasing volume of health data. Methods: This study introduces a novel hybrid edge-cloud framework designed to manage the surge of multidimensional genomic and omics data in the healthcare sector. It combines the localized processing capabilities of edge computing with the scalable resources of cloud computing. Evaluations involved using simulated cytometry datasets to demonstrate the architecture’s effectiveness. Results: The implementation of the hybrid edge-cloud framework demonstrated improvements in key performance metrics. Network efficiency was enhanced by reducing data transfer latency through localized edge processing. Operational costs were minimized using advanced compression techniques, with the Zstandard (ZSTD) codec significantly reducing data size and improving upload times. The framework also ensured enhanced data privacy by leveraging edge-based anonymization techniques, which process sensitive information locally before transfer to the cloud. These findings highlight the framework’s ability to optimize largescale omics data management through innovative approaches, achieving significant gains in scalability and security. Conclusion: Integrating edge computing into a cloud-based omics data management framework significantly enhances processing efficiency, reduces data size, and speeds up upload times. This approach offers a transformative potential for omics and genomic data processing in healthcare, with a balanced emphasis on
efficiency, cost, and privacy.