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Fault-Tolerant Incremental Diagnosis with Limited Historical Data
SICS.
SICS.
SICS.
2008 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We describe a novel incremental diagnostic system based on a statistical model that is trained from empirical data. The system guides the user by calculating what additional information would be most helpful for the diagnosis. We show that our diagnostic system can produce satisfactory classification rates, using only small amounts of available background information, such that the need of collecting vast quantities of initial training data is reduced. Further, we show that incorporation of inconsistency-checking mechanisms in our diagnostic system reduces the number of incorrect diagnoses caused by erroneous input.

Place, publisher, year, edition, pages
2008.
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:kth:diva-144601DOI: 10.1109/PHM.2008.4711451ISI: 000266719200038Scopus ID: 2-s2.0-58449111615OAI: oai:DiVA.org:kth-144601DiVA: diva2:714330
Conference
The International Conference on Prognostics and Health Management (PHM)
Note

QC 20140509

Available from: 2014-04-27 Created: 2014-04-27 Last updated: 2014-05-09Bibliographically approved
In thesis
1. Probabilistic Fault Management in Networked Systems
Open this publication in new window or tab >>Probabilistic Fault Management in Networked Systems
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Technical advances in network communication systems (e.g. radio access networks) combined with evolving concepts based on virtualization (e.g. clouds), require new management algorithms in order to handle the increasing complexity in the network behavior and variability in the network environment. Current network management operations are primarily centralized and deterministic, and are carried out via automated scripts and manual interventions, which work for mid-sized and fairly static networks. The next generation of communication networks and systems will be of significantly larger size and complexity, and will require scalable and autonomous management algorithms in order to meet operational requirements on reliability, failure resilience, and resource-efficiency.

A promising approach to address these challenges includes the development of probabilistic management algorithms, following three main design goals. The first goal relates to all aspects of scalability, ranging from efficient usage of network resources to computational efficiency. The second goal relates to adaptability in maintaining the models up-to-date for the purpose of accurately reflecting the network state. The third goal relates to reliability in the algorithm performance in the sense of improved performance predictability and simplified algorithm control.

This thesis is about probabilistic approaches to fault management that follow the concepts of probabilistic network management (PNM). An overview of existing network management algorithms and methods in relation to PNM is provided. The concepts of PNM and the implications of employing PNM-algorithms are presented and discussed. Moreover, some of the practical differences of using a probabilistic fault detection algorithm compared to a deterministic method are investigated. Further, six probabilistic fault management algorithms that implement different aspects of PNM are presented.

The algorithms are highly decentralized, adaptive and autonomous, and cover several problem areas, such as probabilistic fault detection and controllable detection performance; distributed and decentralized change detection in modeled link metrics; root-cause analysis in virtual overlays; event-correlation and pattern mining in data logs; and, probabilistic failure diagnosis. The probabilistic models (for a large part based on Bayesian parameter estimation) are memory-efficient and can be used and re-used for multiple purposes, such as performance monitoring, detection, and self-adjustment of the algorithm behavior. 

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2014. 61 p.
Series
TRITA-CSC-A, ISSN 1653-5723 ; 2014:06
Keyword
probabilistic network management; probabilistic modeling; fault management; fault detection; event-correlation; change detection, probabilistisk nätverkshantering; probabilistiska modeller; fel- hantering; feldetektion; korrelationsanalys; förändringsdetektion
National Category
Communication Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-144608 (URN)978-91-7595-114-0 (ISBN)
Public defence
2014-05-28, F3, Lindstedtsvägen 26, KTH, Stockholm, 14:00 (English)
Opponent
Supervisors
Note

QC 20140509

Available from: 2014-05-09 Created: 2014-04-27 Last updated: 2014-05-13Bibliographically approved

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