Complex Event Processing approach to automated monitoring of a particle accelerator and its control system

• Autorzy: Grzegorczyk K. , Baggiolini V. , Zieliński K.

Identyfikatory

DOI

10.7494/csci.2014.15.4.351

• Języki publikacji: EN

Abstrakty

EN

This article presents the design and implementation of a software component for automated monitoring and diagnostic information analysis of a particle accelerator and its control system. The information that is analyzed can be seen as streams of events. A Complex Event Processing (CEP) approach to event processing was selected. The main advantage of this approach is the ability to continuously query data coming from several streams. The presented software component is based on Esper, the most popular open-source implementation of CEP. As a test bed, the control system of the accelerator complex located at CERN, the European Organization for Nuclear Research, was chosen. The complex includes the Large Hadron Collider, the world’s most powerful accelerator. The main contribution to knowledge is by showing that the CEP approach can successfully address many of the challenges associated with automated monitoring of the accelerator and its control system that were previously unsolved. Test results, performance analysis, and a proposal for further works are also presented.

Słowa kluczowe

EN

automated monitoring; Esper; complex event processing

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2014
• Tom: Vol. 15 (4)
• Strony: 351--364
• Opis fizyczny: Bibliogr. 17 poz., rys.

Twórcy

autor

Grzegorczyk K.

• AGH University of Science and Technology, Department of Computer Science, Krakow, Poland

autor

Baggiolini V.

• Beams Department, CERN, the European Organization for Nuclear Research

autor

Zieliński K.

• AGH University of Science and Technology, Department of Computer Science, Krakow, Poland

Bibliografia

• [1] Demers A. J., Gehrke J., Panda B., Riedewald M., Sharma V., White W. M., et al.: Cayuga: A General Purpose Event Monitoring System. In: CIDR, vol. 7, pp. 412–422. 2007.
• [2] Esper. URL http://esper.codehaus.org.
• [3] Frammery B.: The LHC Control System. In: Proceedings of ICALEPCS. pp.10–14. 2005.
• [4] Drools Fusion. URL http://drools.jboss.org/drools-fusion.
• [5] Gerhards R.: The syslog protocol. Tech. rep., IETF, 2009. RFC 5424.
• [6] Kazarov A., Lehmann Miotto G., Magnoni L.: The AAL project: automated monitoring and intelligent analysis for the ATLAS data taking infrastructure. In: Journal of Physics: Conference Series, vol. 368, IOP Publishing, 2012.
• [7] Kordas K., et al.:The ATLAS Data Acquisition and Trigger: concept, design and status . Tech. rep., CERN, Geneva, 2006.
• [8] Luckham D. C.: The power of events, vol. 204. Addison-Wesley Reading, 2002.
• [9] Magnoni L., Lehmann Miotto G., Luppi E.: Intelligent monitoring and fault diagnosis for ATLAS TDAQ: a complex event processing solution. Ph.D. thesis, Ferrara University, 2012, presented 30 Mar 2012.
• [10] Michelson B. M.: Event-driven architecture overview. Tech. rep., Patricia Seybold Group, 2006.
• [11] Roderick C., Billen R., Gaspar Aparicio R.D., Grancher E., Khodabandeh A., Segura Chinchilla N.: The LHC Logging Service : Handling terabytes of on-line data. Tech. rep., CERN, Geneva, 2009.
• [12] Skałkowski K., Zieliński K.: Applying formalized rules for treatment procedures to data delivered by personal medical devices. Journal of biomedical informatics, vol. 46(3), pp. 530–540, 2013.
• [13] Slopper J. E., Mapelli L.: Error Management in ATLAS TDAQ: An Intelligent Systems approach. Ph.D. thesis, Warwick University, Warwick, 2010, presented 2010.
• [14] Splunk Enterprise. URL http://www.splunk.com.
• [15] Teixeira P.H.d.S., Clemente R. G., Kaiser R. A., Vieira Jr D. A.: HOLMES: An event driven solution to monitor data centers through continuous queries and machine learning. In: Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems, pp. 216–221, ACM, 2010.
• [16] Wu E., Diao Y., Rizvi S.: High-performance Complex Event Processing over Streams. In: Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, SIGMOD ’06, pp. 407–418, ACM, 2006.
• [17] Xu W., Huang L., Fox A., Patterson D., Jordan M.I.: Detecting Large-scale System Problems by Mining Console Logs. In: Proceedings of the ACM SIGOPS 22Nd Symposium on Operating Systems Principles, pp. 117–132. 2009.