SRLG identification from time series analysis of link state data

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).