Conveners
Infrastructure Clouds and Virtualisation
- Tomoaki Nakamura (KEK)
Infrastructure Clouds and Virtualisation
- Ludek Matyska (CESNET)
Nowadays Machine Learning (ML) techniques are widely adopted in many areas of High-Energy Physics (HEP) and certainly will play a significant role also in the upcoming High-Luminosity LHC (HL-LHC) upgrade foreseen at CERN. A huge amount of data will be produced by LHC and collected by the experiments, facing challenges at the exascale.
Here, we present Machine Learning as a Service solution...
The Fermi-LAT long-term Transient (FLT) monitoring aim is the routine search of gamma-ray sources on monthly time intervals of Fermi-LAT data.
The FLT analysis consists of two steps: first the monthly data sets were analyzed using a wavelet-based source detection algorithm that provided the candidate new transient sources; finally these transient candidates were analyzed using the standard...
The Compact Muon Solenoid (CMS) experiment heavily relies on the CMSWEB cluster to host critical services for its operational needs. Recently, there has been migration of the CMSWEB cluster from the VM cluster to the Kubernetes (k8s) cluster. The new cluster of CMSWEB in Kubernetes enhances sustainability and reduces the operational cost. In this work, we added new features to the CMSWEB k8s...
The number of scientific communities needing access to large amounts of computing resources for their research work is growing. These demands are largely satisfied by grid computing infrastructures providing a unified access to resources distributed all over the world. A growing portion of those resources are provided as private clouds. These sites have different access protocols compared to...
Machine Learning and Artificial Intelligence tools have become more and more commonplace in research over the last years and a growing need for organising models and source codes emerges. For the latter task, there are several version control tools, of which git is the de-facto standard. Together with Continuous Integration and Continuous Deployment (CI/CD) pipelines, however, git unfolds a...
The INFN Tier-1 data centre is the main italian computing site for scientific communities on High Energy Physics and astroparticle research. Access to the resources is arbitrated by a HTCondor batch system which is in charge of balancing the overall usage by several competing user groups according to their agreed quotas. The set of different workloads submitted to the computing cluster is...
The Italian WLCG Tier-1 located in Bologna and managed by INFN provides batch computing resources to several research communities in the fields of High-Energy Physics, Astroparticle Physics, Gravitational Waves, Nuclear Physics and others. The capability of manually executing jobs in the Computing Nodes managed by the Batch System, as they normally would when using owned or cloud resources, is...
Starting at the end of 2019, INFN has been restructuring its internal organization and solutions for what regards support to computing and storage resources and services. One of the first tangible results of this effort has been the creation of INFN Cloud, a data lake-centric, heterogeneous federated Cloud infrastructure. Building on the INFN experience gained in the past 20 years with the...
