TY - GEN
T1 - Provenance-based prediction scheme for object storage system in HPC
AU - Dai, Dong
AU - Chen, Yong
AU - Kimpe, Dries
AU - Ross, Rob
PY - 2014
Y1 - 2014
N2 - Object-based storage model is recently widely adopted both in industry and academia to support growingly data intensive applications in high-performance computing. However, the I/O prediction strategies which have been proven effective in traditional parallel file systems, have not been thoroughly studied under this new object-based storage model. There are new challenges introduced from object storage that make traditional prediction systems not work properly. In this paper, we propose a new I/O access prediction system based on provenance analysis on both applications and objects. We argue that the provenance, which contains metadata that describes the history of data, reveals the detailed information about applications and data sets, which can be used to capture the system status and provide accurate I/O prediction efficiently. Our current evaluations based on real-world trace data (Darshan datasets) simulation also confirm that provenance-based prediction system is able to provide accurate predictions for object storage systems.
AB - Object-based storage model is recently widely adopted both in industry and academia to support growingly data intensive applications in high-performance computing. However, the I/O prediction strategies which have been proven effective in traditional parallel file systems, have not been thoroughly studied under this new object-based storage model. There are new challenges introduced from object storage that make traditional prediction systems not work properly. In this paper, we propose a new I/O access prediction system based on provenance analysis on both applications and objects. We argue that the provenance, which contains metadata that describes the history of data, reveals the detailed information about applications and data sets, which can be used to capture the system status and provide accurate I/O prediction efficiently. Our current evaluations based on real-world trace data (Darshan datasets) simulation also confirm that provenance-based prediction system is able to provide accurate predictions for object storage systems.
KW - I/O Prediction
KW - Object Storage
KW - Provenance
UR - http://www.scopus.com/inward/record.url?scp=84904554849&partnerID=8YFLogxK
U2 - 10.1109/CCGrid.2014.27
DO - 10.1109/CCGrid.2014.27
M3 - Conference contribution
AN - SCOPUS:84904554849
SN - 9781479927838
T3 - Proceedings - 14th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2014
SP - 550
EP - 551
BT - Proceedings - 14th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2014
PB - IEEE Computer Society
T2 - 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2014
Y2 - 26 May 2014 through 29 May 2014
ER -