Pulse Brain · Growing Health Evidence Index
Peer-reviewed

A Scalable Online Monitoring System Based on Elasticsearch for Distributed Data Acquisition in Cms

Jean‐Marc André, U. Behrens, J. G. Branson, Philipp Brummer, Olivier Chaze, Sergio Cittolin, Diego da Silva Gomes, Georgiana-Lavinia Darlea, Christian Deldicque, Z. Demiragli, Marc Dobson, Nicolas Doualot, S. Erhan, Richard Fulcher Jonathan, D. Gigi, Maciej Gładki, F. Glege, G. Gomez Ceballos, J. Hegeman, André Holzner, M. Janulis, Michael Lettrich, Audrius Mečionis, Frans Meijers, E. Meschi, R. K. Mommsen, Srećko Morović, Vivian O’Dell, L. Orsini, I. Papakrivopoulos, Christoph M. E. Paus, Petia Petrova, Andrea Petrucci, M. Pieri, Dinyar Rabady, A. Rácz, V. Rapševičius, T. Reis, Hannes Sakulin, Christoph Schwick, Dainius Šimelevičius, Mantas Stankevičius, Cristina Vazquez Velez, Michail Vougioukas, Christian Wernet, Petr Zejdl

EPJ Web of Conferences · 2019

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Summary

The part of the CMS Data Acquisition (DAQ) system responsible for data readout and event building is a complex network of interdependent distributed applications. To ensure successful data taking, these programs have to be constantly monitored in order to facilitate the timeliness of necessary corrections in case of any deviation from specified behaviour. A large number of diverse monitoring data samples are periodically collected from multiple sources across the network. Monitoring data are kept in memory for online operations and optionally stored on disk for post-mortem analysis. We present a generic, reusable solution based on an open source NoSQL database, Elasticsearch, which is fully compatible and non-intrusive with respect to the existing system. The motivation is to benefit from

Source type
Peer-reviewed study
DOI
10.1051/epjconf/201921401048
Catalogue ID
BFmoakviqe-trmlb1
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