Modern experiments in High Energy and Nuclear Physics heavily rely on distributed computations using multiple computational facilities across the world. One of the essential types of the computations is a distributed data production where petabytes of raw files from a single source has to be processed once (per production campaign) using thousands of CPUs at distant locations and the output has to be transferred back to that source. The data distribution over a large system does not necessary match the distribution of storage, network and CPU capacity. Therefore, bottlenecks may appear and lead to increased latency and degraded performance. In this paper we propose a new scheduling approach for distributed data production which is based on the network flow maximization model. In our approach a central planner defines how much input and output data should be transferred over each network link in order to maximize the computational throughput. Such plans are created periodically for a fixed planning time interval using up-to-date information on network, storage and CPU resources. The centrally created plans are executed in a distributed manner by dedicated services running at participating sites. In conclusion, our simulations based on the log records from the data production framework of the experiment STAR (Solenoid Tracker at RHIC) have shown that the proposed model systematically provides a better performance compared to the simulated traditional techniques.
Makatun, Dzmitry, et al. "Planning of distributed data production for High Energy and Nuclear Physics." Cluster Computing, vol. 21, no. 4, Aug. 2018. https://doi.org/10.1007/s10586-018-2834-3
Makatun, Dzmitry, Lauret, Jérôme, & Rudová, Hana (2018). Planning of distributed data production for High Energy and Nuclear Physics. Cluster Computing, 21(4). https://doi.org/10.1007/s10586-018-2834-3
Makatun, Dzmitry, Lauret, Jérôme, and Rudová, Hana, "Planning of distributed data production for High Energy and Nuclear Physics," Cluster Computing 21, no. 4 (2018), https://doi.org/10.1007/s10586-018-2834-3
@article{osti_1480983,
author = {Makatun, Dzmitry and Lauret, Jérôme and Rudová, Hana},
title = {Planning of distributed data production for High Energy and Nuclear Physics},
annote = {Modern experiments in High Energy and Nuclear Physics heavily rely on distributed computations using multiple computational facilities across the world. One of the essential types of the computations is a distributed data production where petabytes of raw files from a single source has to be processed once (per production campaign) using thousands of CPUs at distant locations and the output has to be transferred back to that source. The data distribution over a large system does not necessary match the distribution of storage, network and CPU capacity. Therefore, bottlenecks may appear and lead to increased latency and degraded performance. In this paper we propose a new scheduling approach for distributed data production which is based on the network flow maximization model. In our approach a central planner defines how much input and output data should be transferred over each network link in order to maximize the computational throughput. Such plans are created periodically for a fixed planning time interval using up-to-date information on network, storage and CPU resources. The centrally created plans are executed in a distributed manner by dedicated services running at participating sites. In conclusion, our simulations based on the log records from the data production framework of the experiment STAR (Solenoid Tracker at RHIC) have shown that the proposed model systematically provides a better performance compared to the simulated traditional techniques.},
doi = {10.1007/s10586-018-2834-3},
url = {https://www.osti.gov/biblio/1480983},
journal = {Cluster Computing},
issn = {ISSN 1386-7857},
number = {4},
volume = {21},
place = {United States},
publisher = {Springer},
year = {2018},
month = {08}}
IEEE Communications Society Workshop on Quality of Service, 2000 Eighth International Workshop on Quality of Service. IWQoS 2000 (Cat. No.00EX400)https://doi.org/10.1109/IWQOS.2000.847954
Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 499, Issue 2-3https://doi.org/10.1016/S0168-9002(02)01960-5
2014 22nd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), 2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processinghttps://doi.org/10.1109/PDP.2014.49
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication - SIGCOMM '99https://doi.org/10.1145/316188.316229
Kliazovich, Dzmitry; Bouvry, Pascal; Khan, Samee Ullah
Int'l Conference on Cyber, Physical and Social Computing (CPSCom), 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computinghttps://doi.org/10.1109/greencom-cpscom.2010.31
Proceedings of the ACM SIGCOMM 2012 conference on Applications, technologies, architectures, and protocols for computer communication - SIGCOMM '12https://doi.org/10.1145/2342356.2342397