Repurposing of the Run 2 CMS High Level Trigger Infrastructure as a Cloud Resource for Offline Computing
<|reference_start|>Repurposing of the Run 2 CMS High Level Trigger Infrastructure as a Cloud Resource for Offline Computing: The former CMS Run 2 High Level Trigger (HLT) farm is one of the largest contributors to CMS compute resources, providing about 25k job slots for offline computing. This CPU farm was initially employed as an opportunistic resource, exploited during inter-fill periods, in the LHC Run 2. Since then, it has become a nearly transparent extension of the CMS capacity at CERN, being located on-site at the LHC interaction point 5 (P5), where the CMS detector is installed. This resource has been configured to support the execution of critical CMS tasks, such as prompt detector data reconstruction. It can therefore be used in combination with the dedicated Tier 0 capacity at CERN, in order to process and absorb peaks in the stream of data coming from the CMS detector. The initial configuration for this resource, based on statically configured VMs, provided the required level of functionality. However, regular operations of this cluster revealed certain limitations compared to the resource provisioning and use model employed in the case of WLCG sites. A new configuration, based on a vacuum-like model, has been implemented for this resource in order to solve the detected shortcomings. This paper reports about this redeployment work on the permanent cloud for an enhanced support to CMS offline computing, comparing the former and new models' respective functionalities, along with the commissioning effort for the new setup.<|reference_end|>
@article{mascheroni2024repurposing,
    title={Repurposing of the Run 2 CMS High Level Trigger Infrastructure as a
  Cloud Resource for Offline Computing},
    author={Marco Mascheroni, Antonio Perez-Calero Yzquierdo, Edita Kizinevic,
  Farrukh Aftab Khan, Hyunwoo Kim, Maria Acosta Flechas, Nikos Tsipinakis,
  Saqib Haleem, Damiele Spiga, Christoph Wissing and Frank Wurthwein},
    journal={arXiv preprint arXiv:2405.14639},
    year={2024},
    doi={10.1051/epjconf/202429503036},
    archivePrefix={arXiv},
    eprint={2405.14639},
    primaryClass={cs.DC}
}