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Enhancing CTAO monitoring and alarm subsystems in distributed environments using ServiMon

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F25%3A00648264" target="_blank" >RIV/68378271:_____/25:00648264 - isvavai.cz</a>

  • Result on the web

    <a href="https://pos.sissa.it/501/775/pdf" target="_blank" >https://pos.sissa.it/501/775/pdf</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.22323/1.501.0775" target="_blank" >10.22323/1.501.0775</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Enhancing CTAO monitoring and alarm subsystems in distributed environments using ServiMon

  • Original language description

    ServiMon is a scalable data collection and auditing pipeline designed for service-oriented, cost-efficient quality control in distributed environments, including the CTAO monitoring, logging, and alarm subsystems. Developed within a Docker-based architecture, it leverages cloud-native technologies and distributed computing principles to enhance system observability and reliability. At its core, ServiMon integrates key technologies such as Prometheus, Grafana, Kafka, and Cassandra. Prometheus serves as the primary engine for real-time performance metric collection, enabling efficient monitoring across multiple nodes. Grafana provides interactive, service-oriented data visualization, facilitating system performance analysis. Additionally, Kafka and Cassandra expose system metrics via the JMX Exporter, offering critical insights into infrastructure availability and performance. This contribution exposes how ServiMon could provide an enhancement on scalability, security, and efficiency in a distributed computing environment, such as the CTAO monitoring, logging, and alarm subsystems. This integrated approach not only ensures robust real-time monitoring, but also optimizes operational costs. Furthermore, ServiMon’s ability to generate large volumes of diverse data over time provides a strong foundation for predictive maintenance. By incorporating stochastic and approximate computing techniques, it enables proactive failure detection and system optimization, minimizing downtime and maximizing telescope availability.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10303 - Particles and field physics

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2025

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of Science

  • ISBN

  • ISSN

    1824-8039

  • e-ISSN

  • Number of pages

    8

  • Pages from-to

    775

  • Publisher name

    Sissa Medilab srl

  • Place of publication

    Trieste

  • Event location

    Ženeva

  • Event date

    Jul 15, 2025

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article