The Science DMZ: A Network Design Pattern for Data-Intensive Science

The ever-increasing scale of scientific data has become a significant challenge for researchers that rely on networks to interact with remote computing systems and transfer results to collaborators worldwide. Despite the availability of high-capacity connections, scientists struggle with inadequate cyberinfrastructure that cripples data transfer performance, and impedes scientific progress. The Science DMZ paradigm comprises a proven set of network design patterns that collectively address these problems for scientists. We explain the Science DMZ model, including network architecture, system configuration, cybersecurity, and performance tools, that creates an optimized network environment for science. We describe use cases from universities, supercomputing centers and research laboratories, highlighting the effectiveness of the Science DMZ model in diverse operational settings. In all, the Science DMZ model is a solid platform that supports any science workflow, and flexibly accommodates emerging network technologies. As a result, the Science DMZ vastly improves collaboration, accelerating scientific discovery

The Science DMZ: A Network Design Pattern for Data-Intensive Science

The ever-increasing scale of scientific data has become a significant challenge for researchers that rely on networks to interact with remote computing systems and transfer results to collaborators worldwide. Despite the availability of high-capacity connections, scientists struggle with inadequate cyberinfrastructure that cripples data transfer performance, and impedes scientific progress. The Science DMZ paradigm comprises a proven set of network design patterns that collectively address these problems for scientists. We explain the Science DMZ model, including network architecture, system configuration, cybersecurity, and performance tools, that creates an optimized network environment for science. We describe use cases from universities, supercomputing centers and research laboratories, highlighting the effectiveness of the Science DMZ model in diverse operational settings. In all, the Science DMZ model is a solid platform that supports any science workflow, and flexibly accommodates emerging network technologies. As a result, the Science DMZ vastly improves collaboration, accelerating scientific discovery

ARIES Network Requirements Review

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all of its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. On May 1, 2021, ESnet and the DOE Office of Energy Efficiency and Renewable Energy (EERE), organized an ESnet requirements review of the ARIES (Advanced Research on Integrated Energy Systems) platform. Preparation for this event included identification of key stakeholders to the process: program and facility management, research groups, technology providers, and a number of external observers. These individuals were asked to prepare formal case study documents in order to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward

The Science DMZ: A Network Design Pattern for Data-Intensive Science

The ever-increasing scale of scientific data has become a significant challenge for researchers that rely on networks to interact with remote computing systems and transfer results to collaborators worldwide. Despite the availability of high-capacity connections, scientists struggle with inadequate cyberinfrastructure that cripples data transfer performance, and impedes scientific progress. The Science DMZ paradigm comprises a proven set of network design patterns that collectively address these problems for scientists. We explain the Science DMZ model, including network architecture, system configuration, cybersecurity, and performance tools, that creates an optimized network environment for science. We describe use cases from universities, supercomputing centers and research laboratories, highlighting the effectiveness of the Science DMZ model in diverse operational settings. In all, the Science DMZ model is a solid platform that supports any science workflow, and flexibly accommodates emerging network technologies. As a result, the Science DMZ vastly improves collaboration, accelerating scientific discovery

Report from the 2016 CrossConnects workshop: improving data mobility & management for bioinformatics

Abstract Due to significant declines in the price of genome sequencing technology, the bioinformatics sciences are experiencing a massive upswing in data generation resulting in an increasing need for data distribution and access. The sheer number of biological areas of study, many of which benefit from the scientific breakthroughs of one another, are adding to the increase of shared data usage. The need for effective data management, analysis, and access are becoming more critical. While there are commonalities facing both precision medicine and metagenomics, each area has its own unique challenges and needs. A workshop was held in April 2016 at Lawrence Berkeley National Laboratory that brought together scientists from both fields, along with experts in computing and networking. Presenters and attendees discussed current research and pressing data issues facing the bioinformatics field today and in the near future

2020 High Energy Physics Network Requirements Review Final Report

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy​ (DOE) ​Office​ of​ Science​ (SC)​ and​ delivers​ highly​ reliable​ data​transport ​capabilities​ optimized​ for ​the​ requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all of its laboratories and facilities in the United States and abroad. ESnet is funded and stewarded​ by​ the​ Advanced​ Scientific ​Computing​ Research​ (ASCR)​ program​ and​ managed​ and​operated​ by​ the​ Scientific ​Networking​ Division​ at ​Lawrence​ Berkeley ​National​ Laboratory​ (LBNL). ​ESnet ​is ​widely​ regarded​ as​ a global leader in the research and education networking community. Throughout ​2020,​ESnet​ and​ the ​Office ​of ​High ​Energy​ Physics​ (HEP)​ of ​the ​DOE​ SC​ organized​ an​ ESnet​ requirements ​review​ of ​HEP-supported​ activities.​ Preparation ​for ​this​ event​included​ identification ​of​ key​ stakeholders: program and facility management, research groups, technology providers, and a number of external observers. These individuals were asked to prepare formal case study documents about their relationship to the HEP program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward. A series of pre-planning meetings better prepared case study authors for this task, along with guidance on how the review would proceed in a virtual fashion. ESnet and ASCR use requirements reviews to discuss and analyze current and planned science use cases and anticipated data output of a particular program, user facility, or project to inform ESnet’s strategic planning, including network operations, capacity upgrades, and other service investments. A requirements review comprehensively surveys major science stakeholders’ plans and processes in order to investigate data management requirements over the next 5–10 years