TY - GEN
T1 - How GridFTP pipelining, parallelism and concurrency work
T2 - 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, SCC 2012
AU - Yildirim, Esma
AU - Kim, Jangyoung
AU - Kosar, Tevfik
PY - 2012
Y1 - 2012
N2 - Optimizing the transfer of large files over high-bandwidth networks is a challenging task that requires the consideration of many parameters (e.g. network speed, round-trip time, and current traffic). Unfortunately, this task becomes more complex when transferring datasets comprised of many small files. In this case, the performance of large dataset transfers not only depends on the characteristics of the transfer protocol and network, but also the number and the size distribution of the files that constitute the dataset. GridFTP is the most advanced transfer tool that provides functions to overcome large dataset transfer bottlenecks.Three of the most important parameters of GridFTP are pipelining, parallelism and concurrency. In this study, we research the effects of these three important parameters, provide models for optimization of these parameters, define guidelines and give an algorithm for their practical use for transfer of large datasets of varying size files.
AB - Optimizing the transfer of large files over high-bandwidth networks is a challenging task that requires the consideration of many parameters (e.g. network speed, round-trip time, and current traffic). Unfortunately, this task becomes more complex when transferring datasets comprised of many small files. In this case, the performance of large dataset transfers not only depends on the characteristics of the transfer protocol and network, but also the number and the size distribution of the files that constitute the dataset. GridFTP is the most advanced transfer tool that provides functions to overcome large dataset transfer bottlenecks.Three of the most important parameters of GridFTP are pipelining, parallelism and concurrency. In this study, we research the effects of these three important parameters, provide models for optimization of these parameters, define guidelines and give an algorithm for their practical use for transfer of large datasets of varying size files.
UR - https://www.scopus.com/pages/publications/84876551765
U2 - 10.1109/SC.Companion.2012.73
DO - 10.1109/SC.Companion.2012.73
M3 - Conference contribution
AN - SCOPUS:84876551765
SN - 9780769549569
T3 - Proceedings - 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, SCC 2012
SP - 506
EP - 515
BT - Proceedings - 2012 SC Companion
Y2 - 10 November 2012 through 16 November 2012
ER -