The Effect of Network Performance on High Energy Physics Computing

Jukka Kommeri, Aleksi Vartiainen, Seppo Heikkilä, Tapio Niemi


High Energy Physics (HEP) data analysis consists of simulating and analysing events in particle physics. In order to understand physics phenomena, one must collect and go through a very large quantity of data generated by particle accelerators and software simulations. This data analysis can be done using the cloud computing paradigm in distributed computing environment where data and computation can be located in different, geographically distant, data centres. This adds complexity and overhead to networking. In this paper, we study how the networking solution and its performance affects the efficiency and energy consumption of HEP computing. Our results indicate that higher latency both prolongs the processing time and increases the energy consumption.


  1. Andrade, P., Bell, T., van Eldik, J., McCance, G., PanzerSteindel, B., dos Santos, M. C., Traylen, S., , and Schwickerath, U. (2012). Review of cern data centre infrastructure. Journal of Physics: Conference Series, 396(4).
  2. Antcheva, I., Ballintijn, M., Bellenot, B., and Biskup, M. (2009). ROOT - A C++ framework for petabyte data storage, statistical analysis and visualization. Computer Physics Communications, 180(12):2499-2512.
  3. Bauerdick, L. A. T., Bloom, K., Bockelman, B., Bradley, D. C., Dasu, S., Dost, J. M., Sfiligoi, I., Tadel, A., Tadel, M., Wuerthwein, F., Yagil, A., and the Cms collaboration (2014). Xrootd, disk-based, caching proxy for optimization of data access, data placement and data replication. Journal of Physics: Conference Series, 513(4).
  4. Behrmann, G., Ozerov, D., and Zanger, T. (2010). Xrootd in dcache - design and experiences. In International Conference on Computing in High Energy and Nuclear Physics (CHEP).
  5. Bird, I., Buncic, P., Carminati, F., Cattaneo, M., Clarke, P., Fisk, I., Girone, M., Harvey, J., Kersevan, B., Mato, Bolla, R., Bruschi, R., Davoli, F., and Cucchietti, F. (2011). Energy efficiency in the future internet: A survey of existing approaches and trends in energy-aware fixed network infrastructures. Communications Surveys Tutorials, IEEE, 13(2):223-244.
  6. Bullot, H., Les Cottrell, R., and Hughes-Jones, R. (2003). Evaluation of advanced tcp stacks on fast longdistance production networks. Journal of Grid Computing, 1(4):345-359.
  7. de Witt, S. and Lahiff, A. (2014). Quantifying xrootd scalability and overheads. Journal of Physics: Conference Series, 513(3).
  8. Dean, J. and Ghemawat, S. (2008). Mapreduce: simplified data processing on large clusters. Commun. ACM, 51(1):107-113.
  9. Dorigo, A., Elmer, P., Furano, F., and Hanushevsky, A. (2005). Xrootd-a highly scalable architecture for data access. WSEAS Transactions on Computers, 1(4.3).
  10. Expsito, R. R., Taboada, G. L., Ramos, S., Tourio, J., and Doallo, R. (2013). Performance analysis of HPC applications in the cloud. Future Generation Computer Systems, 29(1):218 - 229. Including Special section: AIRCC-NetCoM 2009 and Special section: Clouds and Service-Oriented Architectures.
  11. Fabozzi, F., Jones, C., Hegner, B., and Lista, L. (2008). Physics analysis tools for the cms experiment at lhc. Nuclear Science, IEEE Transactions on, 55:3539- 3543.
  12. Gardner, R., Campana, S., Duckeck, G., Elmsheuser, J., Hanushevsky, A., Hönig, F. G., Iven, J., Legger, F., Vukotic, I., Yang, W., and the Atlas Collaboration (2014). Data federation strategies for atlas using xrootd. Journal of Physics: Conference Series, 513(4):042049.
  13. Ha, S., Rhee, I., and Xu, L. (2008). Cubic: A new tcpfriendly high-speed tcp variant. SIGOPS Oper. Syst. Rev., 42(5):64-74.
  14. Haeussler, M., Raney, B. J., Hinrichs, A. S., Clawson, H., Zweig, A. S., Karolchik, D., Casper, J., Speir, M. L., Haussler, D., and Kent, W. J. (2015). Navigating protected genomics data with ucsc genome browser in a box. Bioinformatics, 31(5):764-766.
  15. Hähnel, M., Döbel, B., Völp, M., and Härtig, H. (2012). Measuring energy consumption for short code paths using rapl. ACM SIGMETRICS Performance Evaluation Review, 40(3):13-17.
  16. Kliazovich, D., Bouvry, P., and Khan, S. U. (2010). Greencloud: a packet-level simulator of energy-aware cloud computing data centers. The Journal of Supercomputing, 62(3):1263-1283.
  17. Kuo, J.-J., Yang, H.-H., and Tsai, M.-J. (2014). Optimal approximation algorithm of virtual machine placement for data latency minimization in cloud systems. In INFOCOM, 2014 Proceedings IEEE, pages 1303-1311.
  18. Matsunaga, H., Isobe, T., Mashimo, T., Sakamoto, H., and Ueda, I. (2010). Managed Grids and Cloud Systems in the Asia-Pacific Research Community , chapter Performance of a disk storage system at a Tier-2 site, pages 85-97. Springer US, Boston, MA.
  19. Mauch, V., Kunze, M., and Hillenbrand, M. (2013). High performance cloud computing. Future Generation Computer Systems, 29(6):1408 - 1416.
  20. Meusel, R., Blomer, J., Buncic, P., Ganis, G., and Heikkilä, S. S. (2015). Recent developments in the cernvm-file system server backend. Journal of Physics: Conference Series, 608(1):012031.
  21. O'Luanaigh, C. (2014). Openstack boosts tier 0 for lhc run 2. Technical report, CERN.
  22. Piao, J. T. and Yan, J. (2010). A network-aware virtual machine placement and migration approach in cloud computing. In Grid and Cooperative Computing (GCC), 2010 9th International Conference on, pages 87-92.
  23. Ponce, S. and Hersch, R. D. (2004). Parallelization and scheduling of data intensive particle physics analysis jobs on clusters of pcs. In 18th International Parallel and Distributed Processing Symposium (IPDPS 2004), Santa Fe, New Mexico, USA.
  24. Reano, C., Mayo, R., Quintana-Orti, E., Silla, F., Duato, J., and Pena, A. (2013). Influence of infiniband fdr on the performance of remote gpu virtualization. In IEEE International Conference on Cluster Computing (CLUSTER), pages 1-8.
  25. Shea, R., Wang, F., Wang, H., and Liu, J. (2014). A deep investigation into network performance in virtual machine based cloud environments. In Proceedings of IEEE INFOCOM, pages 1285-1293.

Paper Citation

in Harvard Style

Kommeri J., Vartiainen A., Heikkilä S. and Niemi T. (2016). The Effect of Network Performance on High Energy Physics Computing . In Proceedings of the Sixth International Symposium on Business Modeling and Software Design - Volume 1: BMSD, ISBN 978-989-758-190-8, pages 227-234. DOI: 10.5220/0006224202270234

in Bibtex Style

author={Jukka Kommeri and Aleksi Vartiainen and Seppo Heikkilä and Tapio Niemi},
title={The Effect of Network Performance on High Energy Physics Computing},
booktitle={Proceedings of the Sixth International Symposium on Business Modeling and Software Design - Volume 1: BMSD,},

in EndNote Style

JO - Proceedings of the Sixth International Symposium on Business Modeling and Software Design - Volume 1: BMSD,
TI - The Effect of Network Performance on High Energy Physics Computing
SN - 978-989-758-190-8
AU - Kommeri J.
AU - Vartiainen A.
AU - Heikkilä S.
AU - Niemi T.
PY - 2016
SP - 227
EP - 234
DO - 10.5220/0006224202270234