CERN Accelerating science

CMS Note
Report number	CMS-CR-2023-145
Title	Towards a container-based architecture for CMS data acquisition
Author(s)	Amoiridis, Vasileios (CERN) ; Behrens, Ulf (Rice U.) ; Bocci, Andrea (CERN) ; Branson, James (UC, San Diego) ; Brummer, Philipp Maximilian (CERN) ; Cano, Eric (CERN) ; Cittolin, Sergio (UC, San Diego) ; Quintanilha, Joao (CERN) ; Darlea, Georgiana Lavinia (MIT) ; Deldicque, Christian (CERN) ; Dobson, Marc (CERN) ; Dvorak, Antonin (CERN) ; Gigi, Dominique (CERN) ; Glege, Frank (CERN) ; Gomez Ceballos, Guillelmo (MIT) ; Gorniak, Patrycja Ewa (CERN) ; Gutic, Neven (CERN) ; Hegeman, Jeroen Guido (CERN) ; Da Silva Gomes, Diego (MIT) ; James, Thomas Owen (CERN) ; Karimeh, Wassef (CERN) ; Kartalas, Miltiadis (CERN) ; Krawczyk, Rafal Dominik (Rice U.) ; Li, Wei (Rice U.) ; Long, Kenneth (Imperial Coll., London) ; Meijers, Franciscus (CERN) ; Meschi, Emilio (CERN) ; Morovic, Srecko (UC, San Diego) ; Orsini, Luciano (CERN) ; Paus, Christoph Maria Ernst (MIT) ; Petrucci, Andrea (UC, San Diego) ; Pieri, Marco (UC, San Diego) ; Rabady, Dinyar Sebastian (CERN) ; Racz, Attila (CERN) ; Rizopoulos, Theodoros (CERN) ; Sakulin, Hannes (CERN) ; Schwick, Christoph (CERN) ; Simelevicius, Dainius (Vilnius U.) ; Tzanis, Polyneikis (CERN) ; Vazquez Velez, Cristina (CERN) ; Zejdl, Petr (CERN) ; Zhang, Yousen (Rice U.) ; Zogatova, Dominika (CERN)
Publication	2024
Imprint	31 Aug 2023
Number of pages	9
Published in:	EPJ Web Conf. 295 (2024) 02031
Presented at	26th International Conference on Computing in High Energy & Nuclear Physics, Norfolk, Virginia, Us, 8 - 12 May 2023
DOI	10.1051/epjconf/202429502031
Subject category	Detectors and Experimental Techniques
Accelerator/Facility, Experiment	CERN LHC ; CMS
Keywords	General
Abstract	The CMS data acquisition (DAQ) is implemented as a service-oriented architecture where DAQ applications, as well as general applications such as monitoring and error reporting, are run as self-contained services. The task of deployment and operation of services is achieved by using several heterogeneous facilities, custom configuration data and scripts in several languages. In this work, we restructure the existing system into a homogeneous, scalable cloud architecture adopting a uniform paradigm, where all applications are orchestrated in a uniform environment with standardized facilities. In this new paradigm DAQ applications are organized as groups of containers and the required software is packaged into container images. Automation of all aspects of coordinating and managing containers is provided by the Kubernetes environment, where a set of physical and virtual machines is unified in a single pool of compute resources. We demonstrate that a container-based cloud architecture provides an across-the-board solution that can be applied for DAQ in CMS. We show strengths and advantages of running DAQ applications in a container infrastructure as compared to a traditional application model.
Other source	Inspire