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Self-Driving Infrastructure

We need integrated research infrastructure across user facilities, the network, and HPC facilities to be self-guiding and autonomous so that it can be responsive and adapt to the needs of the instrument and the decisions of intelligent algorithms. We are addressing the key research question of whether the Integrated Research Infrastructure can be an “assistant” or “agent” for the scientist – in addition to many challenges in workflows, networking, and computational infrastructure – to truly make the entire DOE complex autonomous.


Large-Scale, Self-Driving WAN Network

Starting with the ESnet wide area network (WAN), we investigate controllers and WAN traffic statistics from science experiments to understand WAN traffic delivery challenges such as long-living flows, TCP performance issues, and underutilized resources. This project uses artificial intelligence (AI) combined with network controllers to support complex end-to-end network connectivity within ESnet. Contact: Mariam Kiran (Kiran on the Web)

Large-scale Self-Driving 5G Network for Science

Wireless technologies and network advances with 5G, and even beyond-5G, are ushering in a new era for the Internet of Things (IoT) with intelligent sensors bringing complex temporal and spatial challenges to the way we do science across multiple DOE-related activities. While bringing many advantages such as connectivity in urban non-wired areas, mmWave technologies, and new upload/download speeds with less latency, these changes also bring unprecedented data demands, new hardware, and the desire to connect across multiple network domains seamlessly. We will be baking artificial intelligence (AI) into individual edge nodes for intelligent edges and connecting to DOE facilities such as network and data centers for intelligent core, forming a new network ecosystem for science. Contact: Anastasiia Butko

Intelligent Automation of Network: Wireless, 5G, and Satellites

As science uses complex edge hardware, there is a need for end-to-end connectivity of edge devices to central ESnet controllers. In this project, we are exploring intent APIs that help connect Open RAN and 5G MEC to the SENSE controller, enabling end-to-end seamless connectivity to ESnet. Contact: Mariam Kiran (Kiran on the Web)

PosEiDon: Intelligent Science Workflows

PosEiDon aims to advance the knowledge of how simulation and machine learning (ML) methodologies can be harnessed and amplified to improve DOE’s computational and data science. PosEiDon will explore the use of simulation, ML, and hybrid methods to predict, understand, and optimize the behavior of complex DOE science workflows (simulation, instrument data analysis, ML, and superfacility) on production of DOE computational and data infrastructure (CDI). The solutions will be developed based on data collected from DOE and NSF testbeds and validated and refined in production CDI. Contact: Mariam Kiran (Kiran on the Web)

Supporting Workflows at NERSC: Understanding Real-Time Considerations and Performance Trade-Offs

We provide insights for future NERSC storage and network architectures based on data lifecycle today, a step toward supporting diverse scientific workflows with programmable interfaces. Contact: Lavanya Ramakrishnan


U.S. Department of Energy to Provide $6 Million for Research on Advanced Networking

August 19, 2021

This week, the U.S. Department of Energy announced a plan to fund five projects in basic research to advance 5G wireless networking for science applications. One of those projects is the “Large-scale, Self-driving 5G Network for Science” led by ESnet Research Scientist Mariam Kiran. Read More »

Machine Learning to Add Another Dimension to ESnet's Toolbox for Predicting Data Patterns

November 6, 2020

A research project called DAPHNE, led by ESnet’s Mariam Kiran, is exploring how artificial intelligence can be used to design and efficiently manage distributed network architectures to improve data transfers, guarantee high-throughput, and improve traffic engineering. Read More »