System performance monitoring of the ALICE data acquisition system with Zabbix

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Abstract

ALICE (A Large Ion Collider Experiment) is a heavy-ion detector studying the physics of strongly interacting matter and the quark-gluon plasma at the CERN LHC (Large Hadron Collider). The ALICE Data-AcQuisition (DAQ) system handles the data flow from the sub-detector electronics to the permanent data storage in the CERN computing center. The DAQ farm consists of about 1000 devices of many different types ranging from direct accessible machines to storage arrays and custom optical links. The system performance monitoring tool used during the LHC run 1 will be replaced by a new tool for run 2. This paper shows the results of an evaluation that has been conducted on six publicly available monitoring tools. The evaluation has been carried out by taking into account selection criteria such as scalability, flexibility, reliability as well as data collection methods and display. All the tools have been prototyped and evaluated according to those criteria. We will describe the considerations that have led to the selection of the Zabbix monitoring tool for the DAQ farm. The results of the tests conducted in the ALICE DAQ laboratory will be presented. In addition, the deployment of the software on the DAQ machines in terms of metrics collected and data collection methods will be described. We will illustrate how remote nodes are monitored with Zabbix by using SNMP-based agents and how DAQ specific metrics are retrieved and displayed. We will also show how the monitoring information is accessed and made available via the graphical user interface and how Zabbix communicates with the other DAQ online systems for notification and reporting. © Published under licence by IOP Publishing Ltd.

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Telesca, A., Carena, F., Carena, W., Chapeland, S., Barroso, V. C., Costa, F., … Haller, B. V. (2014). System performance monitoring of the ALICE data acquisition system with Zabbix. In Journal of Physics: Conference Series (Vol. 513). Institute of Physics Publishing. https://doi.org/10.1088/1742-6596/513/6/062046

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