ExoGENI: A critical step forward for edge cloud computing

After more than eight years in operation, the ExoGENI testbed is in the process of being decommissioned. RENCI researchers played leading roles in building, maintaining, and expanding the testbed, which provided a full-scale cloud system that thousands of researchers have used to test and deploy cutting-edge applications.

ExoGENI is one of two components that made up the NSF-funded Global Environment for Network Innovations (GENI) virtual laboratory project. Growing out of the need for a system to allow reproducible research involving computer science systems, distributed systems, and protocols, GENI was established to provide an open infrastructure for at-scale networking and distributed systems research and education across the U.S.

The ExoGENI testbed helped to pioneer edge cloud computing. This type of distributed computing uses many small computing installations rather than large, centralized computing resources located in a few places. Edge cloud computing speeds up data processing because computation and data storage is performed closer to the sources of data. Today, this computing approach is used for applications ranging from 5G mobile phone networks to autonomous driving.

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RENCI’s Network Research and Infrastructure Group works to advance the nation’s cyberinfrastructure

For more than ten years, the Network Research and Infrastructure Group (NRIG) at RENCI has been developing specialized cyberinfrastructure critical for advancing computer science and a variety of scientific domains. Their projects are helping scientists use large amounts of data to make new discoveries and have enabled important new advances in distributing computing networks, cloud-based systems, and software-defined networks.

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RENCI-developed software helps train computers to read 3D microscopy images of the brain

New tool could help scientists understand brain structure changes underlying conditions such as autism

Scientists can now acquire detailed 3D microscopy images of an entire mouse brain in just hours thanks to technology advances such as the high-speed imaging technique known as light sheet microscopy. Although this new imaging data is providing incredible insights into the relationships between brain structure and disease, behavior and cognition, it also comes with some big analysis challenges.

The images obtained with light sheet microscopy capture subcellular information for the approximately 100 million cells that make up the mouse brain. Making full use of this huge amount of data requires the daunting task of identifying important features such as nuclei in every cell. Although machine learning can help, algorithms must be trained to understand what a nucleus looks like, which requires large numbers of manually labeled nuclei to use as training data.

“Creating the training data is a challenging problem because the images can be noisy, and in some areas of the brain, the nuclei are packed so densely that it is hard to separate them out,” said David Borland,  senior visualization researcher at RENCI and co-PI of the Nuclei Ninja project that is developing a high throughput platform for exploring and analyzing whole brain tissue cleared images. “To solve this problem, we developed the Segmentor software to produce high-quality data for training a machine learning algorithm to perform automatic segmentation.”

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Experts and researchers balance the scales at the NSF Conference on Data Science and Law

Data Science and Law are both disciplines that have perceived high barriers for entry. With data science, outsiders are overwhelmed by the thought of having to understand hard math and complicated computer code, as the Chief Justice of the Supreme Court demonstrated when he called statistical evidence of political gerrymandering “sociological gobbledygook.” With respect to law, computer and data scientists feel unequipped to interpret the fairness and justice of their work and perhaps do not even see it as relevant. Many data practitioners believe, “I am just writing an algorithm. It’s math and data; I’m not responsible for what happens downstream.” 

“As data increasingly affects all aspects of daily life, we cannot continue to let data science exist in a vacuum, without thinking of the legal, ethical, and societal implications that result from that math and data. We are being reminded daily of the inadequacy of legal frameworks and lack of governmental oversight of data protection, privacy, and security,” said Sarah Davis, senior project manager at RENCI. “Similarly, legal practitioners and researchers cannot ignore or willfully misunderstand the opportunities and dangers of a data-centric society. Increasingly, ‘black box’ algorithms will be used to make decisions that may attack privacy rights, violate due process, or discriminate against protected groups.”

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RENCI partners with CUAHSI and others to launch Critical Zone Collaborative Network Hub

Five year cooperative agreement offers opportunity to accelerate research on boundary layers of rock, soil, air, water, and living organisms 

The Consortium of Universities for the Advancement of Hydrologic Science (CUAHSI) has been selected to be the Coordinating Hub for the NSF-funded Critical Zone (CZ) Collaborative Network

Collaborators in this new venture include representatives from RENCI, the US Geological Survey, Pennsylvania State University, Utah State University, and the Lahmont-Doherty Earth Observatory of Columbia University. All members of the team have experience with Critical Zone Science and the previous Critical Zone Observatory Network.

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RENCI to help guide effort to improve the efficiency of drone applications by leveraging edge, in-network, and cloud computing

The Renaissance Computing Institute (RENCI) at the University of North Carolina at Chapel Hill will collaborate on a $749,998, two-year effort to develop new architectures and tools for the safe, efficient, and economic operation of drones. The funding was awarded by the National Science Foundation (NSF).

Led by the University of Massachusetts Amherst, scientists from RENCI, the Information Sciences Institute (ISI) at the University of Southern California (USC), and the University of Missouri, will collaborate on FlyNet, a project that will utilize edge, cloud, and in-network computing to generate crucial data that will help them address a variety of pressing issues presented by drones.

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RENCI to develop advanced network software for AtlanticWave-SDX 2.0

Sharing big data requires big networks. Systems like AtlanticWave-SDX, which connects networks in the U.S., Chile, Brazil, and South Africa, provide specialized infrastructure needed to send vast amounts of scientific data across long distances, helping scientists make the most of powerful data collections.

RENCI scientists contributed to the development of AtlanticWave-SDX, a distributed experimental software-defined exchange (SDX) that uses cutting-edge network technology to facilitate the exchange of data between research and education networks in the U.S. with networks on other continents.

Now, RENCI will play a leading role in software development and testing for AtlanticWave-SDX 2.0. The five-year project, supported by a recent $6.5-million award from the U.S. National Science Foundation (NSF), is led by Florida International University and also includes the University of Southern California.

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ROBOKOP technology offers faster, easier exploration of emerging COVID-19 research

As scientists around the world urgently work to understand the best ways to diagnose and treat COVID-19, quick and easy access to the latest research findings and rapid exploration of emerging data have become critical. RENCI scientists have developed new tools and approaches that can help researchers make important discoveries and answer key questions about COVID-19 in record time.

“These new approaches allow scientists to blend together novel observations and information from recent papers with previously known information that can be used to inform, contextualize, and test new COVID-19 information,” said Chris Bizon, director of analytics and data science at RENCI.

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New digital laboratory helps get COVID-19 analyses up and running quickly

Data analysis and visualization are helping answer a variety of questions about COVID-19 such as who is most at risk, how is the disease spreading, and what approaches might work best for treatments. However, setting up a computer environment to analyze the large amounts of data needed to answer such questions is no easy task. It requires selecting data libraries, software, and hardware and estimating how much memory and computing power will be needed. This process is time consuming and few individuals have the complex skill set needed to accomplish it.

RENCI scientists have developed a new digital data science laboratory called Blackbalsam that can help significantly shorten the planning stage for these efforts with a standardized environment housing computational and data sets for COVID-19 analytics.  

“As COVID-19 progressed, I saw that researchers were conducting analyses and visualization on an increasingly varied set of COVID-19 data,” said Blackbalsam co-author Steven Cox, assistant director of software systems architecture at RENCI. “I realized that it would be very helpful to have an environment that overcomes well-known technological and skill barriers by providing an interface that researchers with statistical, analytical, and visualization skills could use.”

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Professor learns new lessons while teaching during a pandemic

When UNC students left for spring break on March 9, the COVID-19 public health crisis was just heating up. Soon after, UNC administrators made the decision to move to remote teaching and extended the break by a week to give instructors time to prepare. RENCI Deputy Director Ashok Krishnamurthy was one of many UNC professors who made the quick transition to teaching via video conferencing on Zoom.

What course were you teaching when you received notice that classes would all be moved online?

I was teaching a computer science course called Introduction to Scientific Programming that is designed for non-computer science majors. Most of the students take the class to learn programming skills for their day-to-day work or research. My section of the course had about 160 students enrolled.

How easily were you able to convert this class to a virtual format?

Fortunately, the course was relatively easy to adapt to virtual teaching. The UNC Computer Science department, and my colleague John Majikes who was teaching another section of the same course, have set up this course in such a way that taking it online was quite straightforward.

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