TY - GEN
T1 - Modeling distributed platforms from application traces for realistic file transfer simulation
AU - Chai, Anchen
AU - Bazm, Mohammad Mahdi
AU - Camarasu-Pop, Sorina
AU - Glatard, Tristan
AU - Benoit-Cattin, Hugues
AU - Suter, Frederic
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/10
Y1 - 2017/7/10
N2 - Simulation is a fast, controlled, and reproducible way to evaluate new algorithms for distributed computing platforms in a variety of conditions. However, the realism of simulations is rarely assessed, which critically questions the applicability of a whole range of findings. In this paper, we present our efforts to build platform models from application traces, to allow for the accurate simulation of file transfers across a distributed infrastructure. File transfers are key to performance, as the variability of file transfer times has important consequences on the dataflow of the application. We present a methodology to build realistic platform models from application traces and provide a quantitative evaluation of the accuracy of the derived simulations. Results show that the proposed models are able to correctly capture real-life variability and significantly outperform the state-of-The-Art model.
AB - Simulation is a fast, controlled, and reproducible way to evaluate new algorithms for distributed computing platforms in a variety of conditions. However, the realism of simulations is rarely assessed, which critically questions the applicability of a whole range of findings. In this paper, we present our efforts to build platform models from application traces, to allow for the accurate simulation of file transfers across a distributed infrastructure. File transfers are key to performance, as the variability of file transfer times has important consequences on the dataflow of the application. We present a methodology to build realistic platform models from application traces and provide a quantitative evaluation of the accuracy of the derived simulations. Results show that the proposed models are able to correctly capture real-life variability and significantly outperform the state-of-The-Art model.
UR - http://www.scopus.com/inward/record.url?scp=85027467346&partnerID=8YFLogxK
U2 - 10.1109/CCGRID.2017.13
DO - 10.1109/CCGRID.2017.13
M3 - Conference contribution
AN - SCOPUS:85027467346
T3 - Proceedings - 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017
SP - 54
EP - 63
BT - Proceedings - 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017
Y2 - 14 May 2017 through 17 May 2017
ER -