In safety-critical systems such as Air Traffic Control system, SCADA systems, Railways Control Systems, there has been a rapid transition from monolithic systems to highly modular ones, using off-the-shelf hardware and software applications possibly developed by different manufactures. This shift increased the probability that a fault occurring in an application propagates to others with the risk of a failure of the entire safety-critical system. This calls for new tools for the on-line detection of anomalous behaviors of the system, predicting thus a system failure before it happens, allowing the deployment of appropriate mitigation policies. The paper proposes a novel architecture, namely CASPER, for online failure prediction that has the distinctive features to be (i) black-box: no knowledge of applications internals and logic of the system is required (ii) non-intrusive: no status information of the components is used such as CPU or memory usage; The architecture has been implemented to predict failures in a real Air Traffic Control System. CASPER exhibits high degree of accuracy in predicting failures with low false positive rate. The experimental validation shows how operators are provided with predictions issued a few hundred of seconds before the occurrence of the failure.

On-line failure prediction in safety-critical systems / Baldoni, Roberto; Montanari, Luca; Rizzuto, Marco. - In: FUTURE GENERATION COMPUTER SYSTEMS. - ISSN 0167-739X. - STAMPA. - 45:(2015), pp. 123-132. [10.1016/j.future.2014.11.015]

On-line failure prediction in safety-critical systems

BALDONI, Roberto
;
MONTANARI, LUCA
;
2015

Abstract

In safety-critical systems such as Air Traffic Control system, SCADA systems, Railways Control Systems, there has been a rapid transition from monolithic systems to highly modular ones, using off-the-shelf hardware and software applications possibly developed by different manufactures. This shift increased the probability that a fault occurring in an application propagates to others with the risk of a failure of the entire safety-critical system. This calls for new tools for the on-line detection of anomalous behaviors of the system, predicting thus a system failure before it happens, allowing the deployment of appropriate mitigation policies. The paper proposes a novel architecture, namely CASPER, for online failure prediction that has the distinctive features to be (i) black-box: no knowledge of applications internals and logic of the system is required (ii) non-intrusive: no status information of the components is used such as CPU or memory usage; The architecture has been implemented to predict failures in a real Air Traffic Control System. CASPER exhibits high degree of accuracy in predicting failures with low false positive rate. The experimental validation shows how operators are provided with predictions issued a few hundred of seconds before the occurrence of the failure.
2015
Complex distributed systems; Complex event processing; Critical infrastructures; Failure prediction; Machine learning; Hardware and Architecture; Software; Computer Networks and Communications
01 Pubblicazione su rivista::01a Articolo in rivista
On-line failure prediction in safety-critical systems / Baldoni, Roberto; Montanari, Luca; Rizzuto, Marco. - In: FUTURE GENERATION COMPUTER SYSTEMS. - ISSN 0167-739X. - STAMPA. - 45:(2015), pp. 123-132. [10.1016/j.future.2014.11.015]
File allegati a questo prodotto
File Dimensione Formato  
Baldoni_On-line_2015.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.28 MB
Formato Adobe PDF
1.28 MB Adobe PDF   Contatta l'autore
Baldoni_preprint_On-line_2015.pdf

accesso aperto

Note: http://dx.doi.org/10.1016/j.future.2014.11.015
Tipologia: Documento in Pre-print (manoscritto inviato all'editore, precedente alla peer review)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.01 MB
Formato Adobe PDF
1.01 MB Adobe PDF

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/866985
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 41
  • ???jsp.display-item.citation.isi??? 33
social impact