Anomaly detection prototype for log-based predictive maintenance at INFN-CNAF tier-1

Minarini, Francesco (2019) Anomaly detection prototype for log-based predictive maintenance at INFN-CNAF tier-1. [Laurea magistrale], Università di Bologna, Corso di Studio in Fisica [LM-DM270]
Documenti full-text disponibili:
[img] Documento PDF (Thesis)
Disponibile con Licenza: Salvo eventuali più ampie autorizzazioni dell'autore, la tesi può essere liberamente consultata e può essere effettuato il salvataggio e la stampa di una copia per fini strettamente personali di studio, di ricerca e di insegnamento, con espresso divieto di qualunque utilizzo direttamente o indirettamente commerciale. Ogni altro diritto sul materiale è riservato

Download (5MB)

Abstract

Splitting the evolution of HEP from the one of computational resources needed to perform analyses is, nowadays, not possible. Each year, in fact, LHC produces dozens of PetaBytes of data (e.g. collision data, particle simulation, metadata etc.) that need orchestrated computing resources for storage, computational power and high throughput networks to connect centers. As a consequence of the LHC upgrade, the Luminosity of the experiment will increase by a factor of 10 over its originally designed value, entailing a non negligible technical challenge at computing centers: it is expected, in fact, an uprising in the amount of data produced and processed by the experiment. With this in mind, the HEP Software Foundation took action and released a road-map document describing the actions needed to prepare the computational infrastructure to support the upgrade. As a part of this collective effort, involving all computing centres of the Grid, INFN-CNAF has set a preliminary study towards the development of AI driven maintenance paradigm. As a contribution to this preparatory study, this master thesis presents an original software prototype that has been developed to handle the task of identifying critical activity time windows of a specific service (StoRM). Moreover, the prototype explores the viability of a content extraction via Text Processing techniques, applying such strategies to messages belonging to anomalous time windows.

Abstract
Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Minarini, Francesco
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Indirizzo
Curriculum E: Fisica applicata
Ordinamento Cds
DM270
Parole chiave
Unsupervised Learning,Machine Learning,Data analysis,OCSVM,TFIDF,Text Processing,Anomaly Detection,computing
Data di discussione della Tesi
25 Ottobre 2019
URI

Altri metadati

Statistica sui download

Gestione del documento: Visualizza il documento

^