Link State Protocol Data Mining for Shared Risk Link Group Detection

Das, Goutam; Papadimitriou, Dimitri; Tavernier, Wouter; Colle, Didier; Dhaene, Tom; Pickavet, Mario; Demeester, Piet

doi:10.1109/icccn.2010.5560151

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…The SRLG inference algorithm running in the machine learning engine receives link failure event information from the OSPF protocol running in the local routing engine. From the failure history, the algorithm can learn and identify SRLGs [4]. These are then passed to the OSPF instance.…”

Section: Srlg Inferencementioning

confidence: 99%

SRLG Inference in OSPF for Improved Reconvergence after Failures

Puype¹,

Papadimitriou

Das³

et al. 2010

Towards a Service-Based Internet

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Section: Srlg Inferencementioning

confidence: 99%

SRLG Inference in OSPF for Improved Reconvergence after Failures

Puype¹,

Papadimitriou

Das³

et al. 2010

Towards a Service-Based Internet

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“…In terms of SRLG detection, the results obtained by means of this model are promising. However, [4] does not considered the temporal dependence among LSAs to further enhance the probabilistic prediction accuracy. In this paper, we introduce a novel algorithm to enhance the probabilistic Bayesian network model of [4] in order to detect and to identify SRLGs by considering the dependency among the inter-arrival time sequences of LSAs, in addition to their occurrence rate.…”

Section: Related Workmentioning

confidence: 99%

“…Henceforth, in [4], we have introduced a state space based Bayesian network model that enables a more complete and accurate probabilistic detection and identification of SRLGs. In terms of SRLG detection, the results obtained by means of this model are promising.…”

Section: Related Workmentioning

confidence: 99%

SRLG identification from time series analysis of link state data

Das

Papadimitriou²,

Puype

et al. 2011

2011 Third International Conference on Communication Systems and Networks (COMSNETS 2011)

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Abstract-Failing to account for the set of links affected by a simultaneous dependent failure during the re-computation of the routing table entries leads to traffic losses until all failed links have been accounted in the re-computation of these entries. Instead, if the router learns about the existence of Shared Risk Link Groups (SRLGs) from the arriving pattern link state routing information, then decisions regarding SRLG failure can be taken promptly to avoid successive re-computations of alternate shortest paths across the updated topology. In this paper, we propose a mechanism to improve the router recovery time upon occurrence of topological link failures by detecting and identifying the existence of SRLGs from link state routing information exchanged in the routing domain. The proposed model first groups into events individual Link State Advertisements (LSAs) issued by different network nodes (routers) upon link state change; then, it combines this information to find temporal dependence among members of event groups. It further introduces a physical model interpretation derived from the application of the Weibull distribution, to determine the error on the joint probabilities of events resulting from the finite observation sample. This association allows binding the dependence of the identified groups comprising one or more events (associated to SRLG) on the corresponding estimated failure rate. Our simulation results show that the proposed technique to locally detect and identify SRLGs performs sufficiently well to trigger with enough confidence simultaneous routing table updates from the arrival of a reduced set of LSAs (ideally one).

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