Related papers: Managed Network Services for Exascale Data Movemen…
Emerging data-driven scientific workflows are seeking to leverage distributed data sources to understand end-to-end phenomena, drive experimentation, and facilitate important decision-making. Despite the exponential growth of available…
A fundamental challenge in large-scale networked systems viz., data centers and cloud networks is to distribute tasks to a pool of servers, using minimal instantaneous state information, while providing excellent delay performance. In this…
Data intensive applications often involve the analysis of large datasets that require large amounts of compute and storage resources. While dedicated compute and/or storage farms offer good task/data throughput, they suffer low resource…
Astronomy produces extremely large data sets from ground-based telescopes, space missions, and simulation. The volume and complexity of these rich data sets require new approaches and advanced tools to understand the information contained…
Modern data science research can involve massive computational experimentation; an ambitious PhD in computational fields may do experiments consuming several million CPU hours. Traditional computing practices, in which researchers use…
Academic migration is the change of host institution by a researcher, typically aimed at achieving a stronger research profile. Scientific features such as the number of collaborations, the productivity and its research impact tend to be…
Machine learning entails a broad range of techniques that have been widely used in Science and Engineering since decades. High-energy physics has also profited from the power of these tools for advanced analysis of colliders data. It is…
Scientific endeavors such as large astronomical surveys generate databases on the terabyte scale. These, usually multidimensional databases must be visualized and mined in order to find interesting objects or to extract meaningful and…
In this article we describe the migration of event data collected by the COMPASS and HARP experiments at CERN. Together these experiments have over 300TB of physics data stored in Objectivity/DB that had to be transferred to a new data…
The computing systems used by LHC experiments has historically consisted of the federation of hundreds to thousands of distributed resources, ranging from small to mid-size resource. In spite of the impressive scale of the existing…
Power consumption will be a key constraint on the future growth of Distributed High Throughput Computing (DHTC) as used by High Energy Physics (HEP). This makes performance-per-watt a crucial metric for selecting cost-efficient computing…
A distributed data warehouse system is one of the actual issues in the field of astroparticle physics. Famous experiments, such as TAIGA, KASCADE-Grande, produce tens of terabytes of data measured by their instruments. It is critical to…
To cope with the growing demand for wireless data and to extend service coverage, future 5G networks will increasingly rely on the use of low powered nodes to support massive connectivity in diverse set of applications and services [1]. To…
Scientific research in many fields routinely requires the analysis of large datasets, and scientists often employ workflow systems to leverage clusters of computers for their data analysis. However, due to their size and scale, these…
The ever-increasing volumes of scientific data present new challenges for distributed computing and Grid technologies. The emerging Big Data revolution drives exploration in scientific fields including nanotechnology, astrophysics,…
The rapid growth of scientific data is surpassing advancements in computing, creating challenges in storage, transfer, and analysis, particularly at the exascale. While data reduction techniques such as lossless and lossy compression help…
We aim to implement a Big Data/Extreme Computing (BDEC) capable system infrastructure as we head towards the era of Exascale computing - termed SAGE (Percipient StorAGe for Exascale Data Centric Computing). The SAGE system will be capable…
High Performance Computing (HPC) has evolved over the past decades into increasingly complex and powerful systems. Current HPC systems consume several MWs of power, enough to power small towns, and are in fact soon approaching the limits of…
This report summarizes insights from the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science, which convened more than 40 experts from national…
During the last decade or so, we have had a deluge of data from not only science fields but also industry and commerce fields. Although the amount of data available to us is constantly increasing, our ability to process it becomes more and…