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The next generation of ATLAS PanDA Monitoring

Research output: Contribution to conference - Without ISBN/ISSN Conference paperpeer-review

  • Jaroslava Schovancová
  • Kaushik De
  • Alexei Klimentov
  • Peter Allan Love
  • Maxim Potekhin
  • Torre Wenaus
Publication date31/03/2014
Number of pages7
<mark>Original language</mark>English
EventInternational Symposium on Grids and Clouds (ISGC) 2014 - Academia Sinica, Taipei, Taiwan, Province of China
Duration: 23/03/201428/03/2014


ConferenceInternational Symposium on Grids and Clouds (ISGC) 2014
Abbreviated titleISGC 2014
Country/TerritoryTaiwan, Province of China
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For many years the PanDA Workload Management System has been the basis for distributed production and analysis for the ATLAS experiment at the LHC. Since the start of data taking PanDA usage has ramped up steadily, with up to 1M completed jobs/day in 2013. The associated monitoring data volume has been rising as well, to levels that present a new set of challenges in the areas of database scalability and monitoring system performance and efficiency. Outside of ATLAS, the PanDA system is also being used in projects like AMS, LSST and a few others. It currently undergoes a significant redesign, both of the core server components responsible for workload management, brokerage and data access, and of the monitoring part, which is critically important for efficient execution of the workflow in a way that’s transparent to the user and also provides an effective set of tools for operational support. The next generation of the PanDA Monitoring System is designed based on a proven, scalable, industry-standard Web Framework – Django. This allows us to achieve significant versatility and possibilities of customization, which is important to cover the needs of the growing community of PanDA users in a variety of science and technology areas. We describe the design principles of the core Web application, the UI layout of the presentation layer, and the challenges that must be met in order to continue the necessary support of the ATLAS experiment while expanding the scope of applications handled by PanDA.