TY - GEN
T1 - Constellation
T2 - 4th IEEE International Conference on Big Data, Big Data 2016
AU - Vazhkudai, Sudharshan S.
AU - Harney, John
AU - Gunasekaran, Raghul
AU - Stansberry, Dale
AU - Lim, Seung Hwan
AU - Barron, Tom
AU - Nash, Andrew
AU - Ramanathan, Arvind
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016
Y1 - 2016
N2 - Constellation's overarching goal is the federation of information from resources within an extreme-scale scientific collaboration to enable the scalable discovery of data and new knowledge pathways. The resource fabric is comprised of petascale supercomputers and storage systems, users, jobs, datasets and lifecycle artifacts. For an extreme-scale supercomputing center, normal operations can generate hundreds of millions of data products and metadata entries describing the resource fabric. Constellation federates the information extracted from the resources using a custom, transformative science graph network; constructs rich metadata indexes and higher-order derived metadata from the extracted information; and conducts scalable graph analytics to unravel hidden data pathways. Our implementation and deployment for a production, supercomputing facility shows that the graph can scale to more than 750 million vertices, its domain agnostic indexing can answer interesting science queries, and its analytics can aid in structural, topological and temporal analysis to identify usage hotspots.
AB - Constellation's overarching goal is the federation of information from resources within an extreme-scale scientific collaboration to enable the scalable discovery of data and new knowledge pathways. The resource fabric is comprised of petascale supercomputers and storage systems, users, jobs, datasets and lifecycle artifacts. For an extreme-scale supercomputing center, normal operations can generate hundreds of millions of data products and metadata entries describing the resource fabric. Constellation federates the information extracted from the resources using a custom, transformative science graph network; constructs rich metadata indexes and higher-order derived metadata from the extracted information; and conducts scalable graph analytics to unravel hidden data pathways. Our implementation and deployment for a production, supercomputing facility shows that the graph can scale to more than 750 million vertices, its domain agnostic indexing can answer interesting science queries, and its analytics can aid in structural, topological and temporal analysis to identify usage hotspots.
UR - http://www.scopus.com/inward/record.url?scp=85015201492&partnerID=8YFLogxK
U2 - 10.1109/BigData.2016.7840959
DO - 10.1109/BigData.2016.7840959
M3 - Conference contribution
AN - SCOPUS:85015201492
T3 - Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
SP - 3052
EP - 3061
BT - Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
A2 - Ak, Ronay
A2 - Karypis, George
A2 - Xia, Yinglong
A2 - Hu, Xiaohua Tony
A2 - Yu, Philip S.
A2 - Joshi, James
A2 - Ungar, Lyle
A2 - Liu, Ling
A2 - Sato, Aki-Hiro
A2 - Suzumura, Toyotaro
A2 - Rachuri, Sudarsan
A2 - Govindaraju, Rama
A2 - Xu, Weijia
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 5 December 2016 through 8 December 2016
ER -