EPJ Web Conf.
Volume 214, 201923rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018)
|Number of page(s)||8|
|Section||T1 - Online computing|
|Published online||17 September 2019|
A Scalable Online Monitoring System Based on Elasticsearch for Distributed Data Acquisition in Cms
2 CERN Geneva Switzerland
3 University of California, Los Angeles Los Angeles, California USA
4 University of California, San Diego San Diego, California USA
5 FNAL Batavia, Illinois USA
6 Massachusetts Institute of Technology, Cambridge Massachusetts USA
7 Technical University of Athens Athens Greece
8 Rice University, Houston Texas USA
9 Vilnius University Vilnius Lithuania
10 Karlsruhe Institute of Technology Karlsruhe Germany
* Corresponding author: email@example.com
Published online: 17 September 2019
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 an offthe-shelf software to facilitate the development, maintenance and support efforts. Elasticsearch provides failover and data redundancy capabilities as well as a programming language independent JSON-over-HTTP interface. The possibility of horizontal scaling matches the requirements of a DAQ monitoring system. The data load from all sources is balanced by redistribution over an Elasticsearch cluster that can be hosted on a computer cloud. In order to achieve the necessary robustness and to validate the scalability of the approach the above monitoring solution currently runs in parallel with an existing in-house developed DAQ monitoring system.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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