PoS - Proceedings of Science
Volume 293 - International Symposium on Grids and Clouds (ISGC) 2017 (ISGC2017) - Data Management & Big Data
Progress in Machine Learning Studies for the CMS Computing Infrastructure
D. Bonacorsi*, V. Kuznetsov, N. Magini, T. Diotalevi, A. Repečka, Ž. Matonis and K. Kančis
Full text: pdf
Published on: December 06, 2017
Tens of Petabytes of collision and simulated data have been collected and distributed across WLCG sites in Run-1 and Run-2 at LHC. A low latency in transfers among dozens of computing centres is crucial to make an efficient use of the computing resources. Despite on average the desired level of throughput has been successfully achieved to serve the LHC physics programs, it is not uncommon to observe transfer latencies caused by a large variety of causes, from file corruptions to site issues, most of which require operator intervention. To improve on this front, in particular, the CMS experiment equipped the PhEDEx dataset replication system with a system to collect the latency data, and a mechanism to categorise and analyse them promptly, matching them to quick and focussed operators intervention. The transfer latencies data has also been the target of Machine Learning techniques - already used in CMS to study and predict the dataset popularity - and preliminary results on the work in progress in terms of predictability potential of this approach for both applications will be presented and discussed.
DOI: https://doi.org/10.22323/1.293.0023
How to cite

Metadata are provided both in "article" format (very similar to INSPIRE) as this helps creating very compact bibliographies which can be beneficial to authors and readers, and in "proceeding" format which is more detailed and complete.

Open Access
Creative Commons LicenseCopyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.