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Long-Term Adaptation and Distributed Detection of Local Network Changes
SICS.
SICS.
2010 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We present a statistical approach to distributed detection of local latency shifts in networked systems. For this purpose, response delay measurements are performed between neighbouring nodes via probing. The expected probe response delay on each connection is statistically modelled via parameter estimation. Adaptation to drifting delays is accounted for by the use of overlapping models, such that previous models are partially used as input to future models. Based on the symmetric Kullback-Leibler divergence metric, latency shifts can be detected by comparing the estimated parameters of the current and previous models. In order to reduce the number of detection alarms, thresholds for divergence and convergence are used. The method that we propose can be applied to many types of statistical distributions, and requires only constant memory compared to e.g., sliding window techniques and decay functions. Therefore, the method is applicable in various kinds of network equipment with limited capacity, such as sensor networks, mobile ad hoc networks etc. We have investigated the behaviour of the method for different model parameters. Further, we have tested the detection performance in network simulations, for both gradual and abrupt shifts in the probe response delay. The results indicate that over 90% of the shifts can be detected. Undetected shifts are mainly the effects of long convergence processes triggered by previous shifts. The overall performance depends on the characteristics of the shifts and the configuration of the model parameters.

Place, publisher, year, edition, pages
2010.
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-144603DOI: 10.1109/GLOCOM.2010.5684137ISI: 000287977405109Scopus ID: 2-s2.0-79551638217OAI: oai:DiVA.org:kth-144603DiVA: diva2:714332
Conference
IEEE Global Telecommunications Conference (GLOBECOM)
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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