Supporting transfers of science big data over Wide Area Networks (WANs) with Data Transfer Nodes (DTNs) requires optimizing multiple parameters within the underlying infrastructure. New solutions for such data movement require new paradigms and technologies, such as NVMe over Fabrics, which provides high-performance data movement with direct remote NVMe device access over traditional fabrics. However, recent NVMe over Fabrics studies have been limited to local storage fabrics. To support increasing demands for the large volume of science data movement during Supercomputing (SC) conferences, we proposed a SCinet DTN-as-a-Service framework orchestrating the desired optimization to meet users, applications, and providers' requirements. Furthermore, we extend the SCinet DTN-as-a-Service framework to incorporate new techniques, solve optimization issues in data-intensive science and evaluate NVMe over Fabrics with multiple WAN testbeds to examine its performance and discover new opportunities for optimization.
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