EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 245 (2020) EPJ Web Conf., 245 (2020) 07039 References
Open Access
Issue
EPJ Web Conf.
Volume 245, 2020
24th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
Article Number 07039
Number of page(s) 8
Section 7 - Facilities, Clouds and Containers
DOI https://doi.org/10.1051/epjconf/202024507039
Published online 16 November 2020
  1. A. W. Mu’alem and D. G. Feitelson, Utilization, predictability, workloads, and user runtime estimates in scheduling the IBM SP2 with backfilling, in IEEE Transactions on Parallel and Distributed Systems 12(6), pp. 529-543 (2001) [CrossRef] [Google Scholar]
  2. D. Tsafrir, Y. Etsion, D.G. Feitelson, Modeling User Runtime Estimates, in JSSPP 2005 3834, (2005) [Google Scholar]
  3. P. Marshall, K. Keahey and T. Freeman, Elastic Site: Using Clouds to Elastically Extend Site Resources, 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, 43-52 (2010) [Google Scholar]
  4. M. Fischer, E. Kuehn, M. Giffels, M. J. Schnepf, A. Petzold, A. Heiss, Lightweight dynamic integration of opportunistic resources, EPJ Web of Conferences CHEP 2019 proceedings (to be published) [Google Scholar]
  5. M. Giffels, et al. Effective Dynamic Integration and Utilization of Heterogenous Compute Resources, EPJ Web of Conferences CHEP 2019 proceedings (to be published) [Google Scholar]
  6. C. B. Lee, Y. Schwartzman, J. Hardy, A. Snavely, Are User Runtime Estimates Inherently Inaccurate?, In: JSSPP 2004, 3277, (2005) [Google Scholar]
  7. D. Tsafrir, Y. Etsion, and D. G. Feitelson, Backfilling Using System-Generated Predictions Rather than User Runtime Estimates, IEEE Trans. Parallel Distrib. Syst., 18(6), 789–803 (2007) [Google Scholar]
  8. O. Sonmez, N. Yigitbasi, A. Iosup, and D. Epema, Trace-based evaluation of job runtime and queue wait time predictions in grids, in Proceedings of the 18th ACM international symposium on High performance distributed computing (HPDC ’09), 111–120 (2009) [CrossRef] [Google Scholar]
  9. E. Gaussier, D. Glesser, V. Reis and D. Trystram, Improving backfilling by using machine learning to predict running times, SC ’15: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 1-10 (2015) [Google Scholar]
  10. S. Pumma, W. Feng, P. Phunchongharn, S. Chapeland and Tiranee Achalakul, A runtime estimation framework for ALICE, Future Generation Computer Systems 72, pp. 65-77 (2017) [CrossRef] [Google Scholar]
  11. P. Love, M. Alef, S. Dal Pra, A. Di Girolamo, A. Forti, J. Templon, E. Vanvakopoulos, JPCS, 898, 092005 (2017) [Google Scholar]
  12. M. Turilli, M. Santcroos, and S. Jha, A Comprehensive Perspective on Pilot-Job Systems. ACM Comput. Surv. 51(2), (2018) [Google Scholar]
  13. E. Kuehn Online analysis of dynamic streaming data, (2018) doi:10.5445/IR/1000083227 [Google Scholar]
  14. S. Lange Analyse und Vorhersage von Nutzungsverhalten und Ressourcenverbrauch in einer wissenschaftlichen Cloud-Umgebung, (2018) [Google Scholar]
  15. Z. Gantner, L. Drumond, C. Freudenthaler, S. Rendle, and L. Schmidt-Thieme, Learning Attribute-to-Feature Mappings for Cold-Start Recommendations, ICDM ’10, 176–185 (2010) [Google Scholar]
  16. E. Serral, P. Valderas, and V. Pelechano, Improving the Cold-Start Problem in User Task Automation by Using Models at Runtime, in: Pokorny J. et al. (eds) Information Systems Development, (2011) [Google Scholar]
  17. G. Aad, J. Butterworth, J. Thion, U. Bratzler, P. Ratoff, R. Nickerson, J. Seixas, I. Grabowska-Bold, F. Meisel, S. Lokwitz et al., Jinst, 3, S08003 (2008) [Google Scholar]
  18. J. Kennedy, C. Serfon, G. Duckeck, R. Walker, A. Olszewski, S. Nderitu et al., JPCS, 219, 072039 (2010) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.