Big Data-Aware Terabits Networking
Sector: Broadband • Location: United States of America
Source: Grants.gov
The Office of Advanced Scientific Computing Research (ASCR) in the Office of Science (SC) at the U.S. Department of Energy (DOE) hereby announces its interest in receiving grant applications to address the networking challenges of scientific Big Data across the Office of Science. DOE?s scientific instruments, observatories, and supercomputers are increasingly generating complex heterogeneous massi
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Participants
Sponsoring Agency | Obfuscated Data |
Company | Obfuscated Data |
Status
Original status | archived |
Taiyo status | Obfuscated Data |
Taiyo last update | 00-00-0000 |
Available timestamps | 00-00-0000 |
Available timestamp type | Obfuscated Data |
Contact
Contact name | Obfuscated Data |
Phone | 0000000000 |
ObfuscatedData@email.com | |
Address | Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data |
Description
Description | The Office of Advanced Scientific Computing Research (ASCR) in the Office of Science (SC) at the U.S. Department of Energy (DOE) hereby announces its interest in receiving grant applications to address the networking challenges of scientific Big Data across the Office of Science. DOE?s scientific instruments, observatories, and supercomputers are increasingly generating complex heterogeneous massive data sets that are revolutionizing the conduct of science and overwhelming scientific computing and network infrastructures. In particular, these massive data sets are introducing distributed data management challenges that cannot be adequately addressed with current state-of-the-art Internet-based networking and commercial storage system management technologies. This FOA has two main objectives: 1) to conduct research on terabit data-aware network technologies and storage system management to address scientific Big Data infrastructure challenges, a nd 2) to explore innovative theoretical frameworks, network-aware data management concepts, and storage system performance optimization models that will improve our understanding of how complex heterogeneous massive data sets should be organized, stored, and shared. A companion Program Announcement to DOE Laboratories (LAB 13-883) will be posted on the SC Grants and Contracts web site at: http://www.science.doe.gov/grants The full text of the Funding Opportunity Announcement (FOA) is located on FedConnect. Instructions for completing the Grant Application Package are contained in the full text of the FOA which can be obtained at: https://www.fedconnect.net/FedConnect/?doc=DE-FOA-0000883&agency=DOE. |
Original sub-sector | Obfuscated |
Original Currency | USD |
Original budget | 000000000000000 |
Procurement method | Obfuscated Data |
Budget | 000000000000000 |
Location
Region | Obfuscated |
Country | Obfuscated |
State | Obfuscated Data |
County | Obfuscated |
Location | Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data |
Source
Source reliability | High |
Data quality score | 100% |
Source | Obfuscated Data |
URL | obfuscated_data,obfuscateddata.com |
More Details
Project Type | Obfuscated Data |
Article Published Date | Obfuscated Data |
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