TY - GEN
T1 - 4CeeD
T2 - 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017
AU - Nguyen, Phuong
AU - Konstanty, Steven
AU - Nicholson, Todd
AU - OBrien, Thomas
AU - Schwartz-Duval, Aaron
AU - Spila, Timothy
AU - Nahrstedt, Klara
AU - Campbell, Roy H.
AU - Gupta, Indranil
AU - Chan, Michael
AU - McHenry, Kenton
AU - Paquin, Normand
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/10
Y1 - 2017/7/10
N2 - In this paper, we present a data acquisition and analysis framework for materials-To-devices processes, named 4CeeD, that focuses on the immense potential of capturing, accurately curating, correlating, and coordinating materials-To-devices digital data in a real-Time and trusted manner before fully archiving and publishing them for wide access and sharing. In particular, 4CeeD consists of novel services: A curation service for collecting data from microscopes and fabrication instruments, curating, and wrapping of data with extensive metadata in real-Time and in a trusted manner, and a cloud-based coordination service for storing data, extracting meta-data, analyzing and finding correlations among the data. Our evaluation results show that our novel cloud framework can help researchers significantly save time and cost spent on experiments, and is efficient in dealing with high-volume and fast-changing workload of heterogeneous types of experimental data.
AB - In this paper, we present a data acquisition and analysis framework for materials-To-devices processes, named 4CeeD, that focuses on the immense potential of capturing, accurately curating, correlating, and coordinating materials-To-devices digital data in a real-Time and trusted manner before fully archiving and publishing them for wide access and sharing. In particular, 4CeeD consists of novel services: A curation service for collecting data from microscopes and fabrication instruments, curating, and wrapping of data with extensive metadata in real-Time and in a trusted manner, and a cloud-based coordination service for storing data, extracting meta-data, analyzing and finding correlations among the data. Our evaluation results show that our novel cloud framework can help researchers significantly save time and cost spent on experiments, and is efficient in dealing with high-volume and fast-changing workload of heterogeneous types of experimental data.
KW - Cloud Computing
KW - Cyber-Physical Systems
KW - Resource Management
UR - http://www.scopus.com/inward/record.url?scp=85027461256&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027461256&partnerID=8YFLogxK
U2 - 10.1109/CCGRID.2017.51
DO - 10.1109/CCGRID.2017.51
M3 - Conference contribution
AN - SCOPUS:85027461256
T3 - Proceedings - 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017
SP - 11
EP - 20
BT - Proceedings - 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 May 2017 through 17 May 2017
ER -