Presentation

DescriptionTransferring big data over Wide Area Networks (WANs) is challenging because optimization is dependent on the specifics of multiple parameters. Network services, paths, and technologies have different characteristics, including loss rate, latency, and available capacity. Yet, frameworks currently used to configure and orchestrate transfer systems, measure performance, and analyze results have limited capabilities. We propose a framework, DTN-as-a-Service (DaaS), for high-performance network data transfers using and integration of techniques, including virtualization, network provisioning, and performance data analysis. This framework has a modular design for supporting multiple transfer tools, optimizers and orchestrators for the data transfer environment, including Docker and Kubernetes. We present a Jupyter based workflow for high-speed network data transfer in data-intensive science and evaluate the performance of the transfer with a simple programmable visualizer implemented in the framework. With the increase in the number and the capacity of WAN links at the conferences (multiple 100 Gbps WAN circuits), the challenges involved in setting up, testing, debugging, verifying and running applications on high-performance systems connecting to the conference SCinet WAN circuits also increase. The SCinet implementation of the DaaS framework for the conference community allowed users to control hardware, software, and network infrastructure for high-speed network data transfer, primarily for large scale applications. Through the evaluation of the framework in our test setup, we demonstrated that NVMe over Fabrics with TCP is twice as efficient compared to using conventional TCP in high-speed NVMe-to-NVMe transfers. We also implemented a 400 Gbps LAN experiment to evaluate the DaaS framework.