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Toward Decentralized Probabilistic Management
SICS.
2011 (engelsk)Inngår i: IEEE Communications Magazine, ISSN 0163-6804, E-ISSN 1558-1896, Vol. 49, nr 7, s. 80-96Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

In recent years, data communication networks have grown to immense size and have been diversified by the mobile revolution. Existing management solutions are based on a centralized deterministic paradigm, which is appropriate for networks of moderate size operating in relatively stable conditions. However, it is becoming increasingly apparent that these management solutions are not able to cope with the large dynamic networks that are emerging. In this article, we argue that the adoption of a decentralized and probabilistic paradigm for network management will be crucial to meet the challenges of future networks, such as efficient resource usage, scalability, robustness, and adaptability. We discuss the potential of decentralized probabilistic management and its impact on management operations, and illustrate the paradigm by three example solutions for real-time monitoring and anomaly detection.

sted, utgiver, år, opplag, sider
2011. Vol. 49, nr 7, s. 80-96
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-144604DOI: 10.1109/MCOM.2011.5936159ISI: 000292376000010Scopus ID: 2-s2.0-79959961883OAI: oai:DiVA.org:kth-144604DiVA, id: diva2:714333
Merknad

QC 20140509

Tilgjengelig fra: 2014-04-27 Laget: 2014-04-27 Sist oppdatert: 2017-12-05bibliografisk kontrollert
Inngår i avhandling
1. Probabilistic Fault Management in Networked Systems
Åpne denne publikasjonen i ny fane eller vindu >>Probabilistic Fault Management in Networked Systems
2014 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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. 

sted, utgiver, år, opplag, sider
Stockholm: KTH Royal Institute of Technology, 2014. s. 61
Serie
TRITA-CSC-A, ISSN 1653-5723 ; 2014:06
Emneord
probabilistic network management; probabilistic modeling; fault management; fault detection; event-correlation; change detection, probabilistisk nätverkshantering; probabilistiska modeller; fel- hantering; feldetektion; korrelationsanalys; förändringsdetektion
HSV kategori
Forskningsprogram
Datalogi
Identifikatorer
urn:nbn:se:kth:diva-144608 (URN)978-91-7595-114-0 (ISBN)
Disputas
2014-05-28, F3, Lindstedtsvägen 26, KTH, Stockholm, 14:00 (engelsk)
Opponent
Veileder
Merknad

QC 20140509

Tilgjengelig fra: 2014-05-09 Laget: 2014-04-27 Sist oppdatert: 2014-05-13bibliografisk kontrollert

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