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Abstract-The Advanced Networking Initiative (ANI) project from the Energy Services Network provides a 100 Gbps testbed, which offers the opportunity for evaluating applications and middleware used by scientific experiments. This testbed is a prototype of a 100 Gbps wide-area network backbone, which links several Department of Energy (DOE) national laboratories, universities and other research institutions. These scientific experiments involve movement of large datasets for collaborations among researchers at different sites and thus require advanced infrastructure for supporting large and fast data transfers. A 100 Gbps network testbed is a key component of the ANI project and is used for DOE's science research programs. This work presents results towards obtaining maximum throughput in large data transfers by optimizing and fine-tuning scientific applications and middleware to use this advanced infrastructure efficiently. A detailed performance evaluation is discussed measuring both applications, from High Energy Physics (HEP) and from data transfer middleware (GridFTP, Globus Online, Storage Resource Management, XrootD and Squid) at 100 Gbps speeds and 53 ms of latency. Results show that up to 97% efficiency of such high bandwidth high latency network is possible, achieving 80-90 Gbps in most test cases with a peak transfer rate of 100 Gbps.

HEPCloud is rapidly becoming the primary system for provisioning compute resources for all Fermilab-affiliated experiments. In order to reliably meet the peak demands of the next generation of High Energy Physics experiments, Fermilab must plan to elastically expand its computational capabilities to cover the forecasted need. Commercial cloud and allocation-based High Performance Computing (HPC) resources both have explicit and implicit costs that must be considered when deciding when to provision these resources, and at which scale. In order to support such provisioning in a manner consistent with organizational business rules and budget constraints, we have developed a modular intelligent decision support system (IDSS) to aid in the automatic provisioning of resources spanning multiple cloud providers, multiple HPC centers, and grid computing federations. In this paper, we discuss the goals and architecture of the HEPCloud Facility, the architecture of the IDSS, and our early experience in using the IDSS for automated facility expansion both at Fermi and Brookhaven National Laboratory.

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