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Artificial Intelligence in the service of system administrators
<!--HTML-->The LHCb online system relies on a large and heterogeneous IT infrastructure made from thousands of servers on which many different applications are running. They run a great variety of tasks : critical ones such as data taking and secondary ones like web servers. The administratio...
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Lenguaje: | eng |
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2012
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Acceso en línea: | http://cds.cern.ch/record/1460669 |
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author | Haen, Christophe |
author_facet | Haen, Christophe |
author_sort | Haen, Christophe |
collection | CERN |
description | <!--HTML-->The LHCb online system relies on a large and heterogeneous IT infrastructure made from thousands of servers on which many different applications are running. They run a great variety of tasks : critical ones such as data taking and secondary ones like web servers. The administration of such a system and making sure it is working properly represents a very important workload for the small expert-operator team.
Research has been performed to try to automatize (some) system administration tasks, starting in 2001 when IBM defined the so-called “self objectives” supposed to lead to “autonomic computing”. In this context, we present a framework that makes use of artificial intelligence and machine learning to monitor and diagnose at a low level and in a non intrusive way Linux-based systems and their interaction with software. Moreover, the multi agent approach we use, coupled with a "object oriented paradigm" architecture should increase a lot our learning speed, and highlight relations between problems. |
id | cern-1460669 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2012 |
record_format | invenio |
spelling | cern-14606692022-11-02T22:23:34Zhttp://cds.cern.ch/record/1460669engHaen, ChristopheArtificial Intelligence in the service of system administratorsComputing in High Energy and Nuclear Physics (CHEP) 2012Conferences<!--HTML-->The LHCb online system relies on a large and heterogeneous IT infrastructure made from thousands of servers on which many different applications are running. They run a great variety of tasks : critical ones such as data taking and secondary ones like web servers. The administration of such a system and making sure it is working properly represents a very important workload for the small expert-operator team. Research has been performed to try to automatize (some) system administration tasks, starting in 2001 when IBM defined the so-called “self objectives” supposed to lead to “autonomic computing”. In this context, we present a framework that makes use of artificial intelligence and machine learning to monitor and diagnose at a low level and in a non intrusive way Linux-based systems and their interaction with software. Moreover, the multi agent approach we use, coupled with a "object oriented paradigm" architecture should increase a lot our learning speed, and highlight relations between problems.oai:cds.cern.ch:14606692012 |
spellingShingle | Conferences Haen, Christophe Artificial Intelligence in the service of system administrators |
title | Artificial Intelligence in the service of system administrators |
title_full | Artificial Intelligence in the service of system administrators |
title_fullStr | Artificial Intelligence in the service of system administrators |
title_full_unstemmed | Artificial Intelligence in the service of system administrators |
title_short | Artificial Intelligence in the service of system administrators |
title_sort | artificial intelligence in the service of system administrators |
topic | Conferences |
url | http://cds.cern.ch/record/1460669 |
work_keys_str_mv | AT haenchristophe artificialintelligenceintheserviceofsystemadministrators AT haenchristophe computinginhighenergyandnuclearphysicschep2012 |