Author: Roderick, C.
Paper Title Page
MOPHA149 Accelerator Schedule Management at CERN 579
 
  • B. Urbaniec, C. Roderick
    CERN, Geneva, Switzerland
 
  Maximizing the efficiency of operating CERN’s accelerator complex requires careful forward planning, and synchronized scheduling of cross-accelerator events. These schedules are of interest to many people helping them to plan and organize their work. Therefore, this data should be easily accessible, both interactively and programmatically. Development of the Accelerator Schedule Management (ASM) system started in 2017 to address such topics and enable definition, management and publication of schedule data in generic way. The ASM system currently includes three core modules to manage: Yearly accelerator schedules for the CERN Injector complex and LHC; Submission and scheduling of Machine Development (MD) requests with supporting statistics; Submission, approval, scheduling and follow-up of control system changes and their impact. This paper describes the ASM Web application (built with Angular, TypeScript and Java) in terms of: Core scheduling functionality; Integration of external data sources; Provision of programmatic access to schedule data via a language agnostic REST API (allowing other systems to leverage schedule data).  
poster icon Poster MOPHA149 [2.477 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOPHA149  
About • paper received ※ 29 September 2019       paper accepted ※ 10 October 2019       issue date ※ 30 August 2020  
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WEPHA163 NXCALS - Architecture and Challenges of the Next CERN Accelerator Logging Service 1465
 
  • J.P. Wozniak, C. Roderick
    CERN, Geneva, Switzerland
 
  CERN’s Accelerator Logging Service (CALS) is in production since 2003 and stores data from accelerator infrastructure and beam observation devices. Initially expecting 1 TB/year, the Oracle based system has scaled to cope with 2.5 TB/day coming from >2.3 million signals. It serves >1000 users making an average of 5 million extraction requests per day. Nevertheless, with a large data increase during LHC Run 2 the CALS system began to show its limits, particularly for supporting data analytics. In 2016 the NXCALS project was launched with the aim of replacing CALS from Run 3 onwards, with a scalable system using "Big Data" technologies. The NXCALS core is production-ready, based on open-source technologies such as Hadoop, HBase, Spark and Kafka. This paper will describe the NXCALS architecture and design choices, together with challenges faced while adopting these technologies. This includes: write/read performance when dealing with vast amounts of data from heterogenous data sources with strict latency requirements; how to extract, transform and load >1 PB of data from CALS to NXCALS. NXCALS is not CERN-specific and can be relevant to other institutes facing similar challenges.  
poster icon Poster WEPHA163 [1.689 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-WEPHA163  
About • paper received ※ 29 September 2019       paper accepted ※ 09 October 2019       issue date ※ 30 August 2020  
Export • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)