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Experiment Dashboard: the monitoring system for the LHC experiments

Authors:

Pablo P. SaizAuthors Info & Claims

GMW '07: Proceedings of the 2007 workshop on Grid monitoring

Pages 45 - 49

https://doi.org/10.1145/1272680.1272689

Published: 25 June 2007 Publication History

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Abstract

In this paper we present the Experiment Dashboard monitoring system, which is currently in use by four Large Hadron Collider (LHC)[1] experiments. The goal of the Experiment Dashboard is to monitor the activities of the LHC experiments on the distributed infrastructure, providing monitoring data from the virtual organization/user perspectives. All LHC experiments are using various Grid infrastructures (LCG/EGEE, OSG, Nordugrid) with correspondingly various middleware flavors and job submission methods. Providing a uniform and complete view of the various activities like job processing, data movement and publishing, access to distributed databases regardless of the underlying Grid flavor is the challenging task. In this paper we will describe the solutions implemented in the Experiment Dashboard.

References

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LHC homepage, http://lhc.web.cern.ch/lhc/

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The LCG Editorial Board, "LHC Computing Grid Technical Design Report", LCG-TDR-001, CERN-LHCC-2005-024, June 2005

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Enabling Grid for E-sciencE (EGEE), http://www.cern.ch/egee

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Open Science Grid (OSG) Web Page, http://www.opensciencegrid.org/

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Nordic Data Grid Facility (NDGF) Web Page, http://www.ndgf.org/

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R-GMA homepage, http://www.r-gma.org/

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GridIce homepage, http://gridice.forge.cnaf.infn.it/

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SAM homepage, http://sam-docs.web.cern.ch/sam-docs/

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Monitoring Agents Using a Large Integrated Services Infrastructure (MonAlisa), http://monalisa.cern.ch/monalisa.html

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M. Branco, D. Cameron, T. Wenaus, "A Scalable Distributed Data Management System for ATLAS", Conference on Computing in High Energy and Nuclear Physics (CHEP06), February 2006, Mumbai (India)

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Aple homepage, http://goc.grid-support.ac.uk/gridsite/accounting/index.html

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Condor-g homepage, http://www.cs.wisc.edu/condor/condorg/

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Cited By

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Kuehn EFischer MGiffels MJung CPetzold A(2015)Analyzing data flows of WLCG jobs at batch job levelJournal of Physics: Conference Series10.1088/1742-6596/608/1/012017608(012017)Online publication date: 22-May-2015
https://doi.org/10.1088/1742-6596/608/1/012017
Kuehn EFischer MJung CPetzold AStreit A(2014)Monitoring Data Streams at Process Level in Scientific Big Data Batch ClustersProceedings of the 2014 IEEE/ACM International Symposium on Big Data Computing10.1109/BDC.2014.21(90-95)Online publication date: 8-Dec-2014
https://dl.acm.org/doi/10.1109/BDC.2014.21
Maier GSchiffers MKranzlmueller DGaidioz B(2010)Association rule mining on grid monitoring data to detect error sourcesJournal of Physics: Conference Series10.1088/1742-6596/219/7/072041219:7(072041)Online publication date: 7-May-2010
https://doi.org/10.1088/1742-6596/219/7/072041
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Index Terms

Experiment Dashboard: the monitoring system for the LHC experiments
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
2. General and reference
  1. Cross-computing tools and techniques
    1. Performance
    2. Reliability

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GMW '07: Proceedings of the 2007 workshop on Grid monitoring

June 2007

56 pages

ISBN:9781595937162

DOI:10.1145/1272680

General Chair:
Julia Andreeva
CERN, Switzerland

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 25 June 2007

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June 25, 2007

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Kuehn EFischer MGiffels MJung CPetzold A(2015)Analyzing data flows of WLCG jobs at batch job levelJournal of Physics: Conference Series10.1088/1742-6596/608/1/012017608(012017)Online publication date: 22-May-2015
https://doi.org/10.1088/1742-6596/608/1/012017
Kuehn EFischer MJung CPetzold AStreit A(2014)Monitoring Data Streams at Process Level in Scientific Big Data Batch ClustersProceedings of the 2014 IEEE/ACM International Symposium on Big Data Computing10.1109/BDC.2014.21(90-95)Online publication date: 8-Dec-2014
https://dl.acm.org/doi/10.1109/BDC.2014.21
Maier GSchiffers MKranzlmueller DGaidioz B(2010)Association rule mining on grid monitoring data to detect error sourcesJournal of Physics: Conference Series10.1088/1742-6596/219/7/072041219:7(072041)Online publication date: 7-May-2010
https://doi.org/10.1088/1742-6596/219/7/072041
Maier Gvan der Ster DKranzlmuller D(2009)Finding associations in Grid monitoring data2009 10th IEEE/ACM International Conference on Grid Computing10.1109/GRID.2009.5353076(89-96)Online publication date: Oct-2009
https://doi.org/10.1109/GRID.2009.5353076
Fiore SNegro AVadacca SAloisio G(2008)iGRelCProceedings of the 2008 Ninth International Conference on Parallel and Distributed Computing, Applications and Technologies10.1109/PDCAT.2008.75(532-537)Online publication date: 1-Dec-2008
https://dl.acm.org/doi/10.1109/PDCAT.2008.75

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Real-Time Performance Support for Complex Grid Applications

An instrumentation infrastructure for grid workflow applications

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