Phronesis, a diagnosis and recovery tool for system administrators
type: Dataset
description: 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 shared experience approach we use, coupled with an "object oriented paradigm" architecture increases a lot our learning speed, and highlight relations between problems.
identifier: NNT: 2013CLF22387
issued: 2026-05-06T06:51:10.065738
language: en
modified: 2026-05-06T06:51:10.065743
contact point: Organization
  fn: CCSD
keyword: administration-systeme
keyword: apprentissage-par-renforcement
keyword: artificial-intelligence
keyword: autonomic-computing
keyword: cern
keyword: diagnosis
keyword: diagnostiques
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-ohcomputer-science-csother-csoh
keyword: informatique-autonome
keyword: intelligence-artificielle
keyword: lhcb
keyword: linux
keyword: recouvrement
keyword: recovery
keyword: reinforcement-learning
keyword: spiotherengineering-sciences-physicsother
keyword: system-administration
keyword: theses
landing page: https://theses.hal.science/tel-00950700

Phronesis, a diagnosis and recovery tool for system administrators
type: Distribution
format: HTML
issued: 2026-05-06T06:51:10.105196
modified: 2026-05-06T06:51:10.035476
access URL: https://theses.hal.science/tel-00950700

test_moissonnage_selune
type: Agent