Abstract
Large-scale natural disasters cause external disturbances to networking infrastructure that lead to large-scale network-service disruption. To understand the impact of natural disasters to networks, it is important to localize and analyze network-service disruption after natural disasters occur.
This work studies an inference of network-service disruption caused by the real natural disaster, Hurricane Katrina. We perform inference using large-scale Internet measurements and human inputs. We use clustering and feature extraction to reduce data dimensionality of sensory measurements and apply semi-supervised learning to jointly use sensory measurements and human inputs for inference.
Our inference shows that after Katrina, approximately 25% of subnets were inferred as unreachable. We find that 62% of unreachable subnets were small subnets at the edges of networks, and 49% of these unreachabilities occurred after the landfall. The majority (73%) of unreachable subnets lasted longer than four weeks showing that Katrina caused extreme damage on networks and a slow recovery.
Network-service disruption is inevitable after large-scale natural disasters occur. Thus, it is crucial to have effective inference techniques for more understanding of network responses and vulnerabilities to natural disasters.
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Erjongmanee, S., Ji, C., Stokely, J., Hightower, N. (2010). Large-Scale Inference of Network-Service Disruption upon Natural Disasters. In: Gaber, M.M., Vatsavai, R.R., Omitaomu, O.A., Gama, J., Chawla, N.V., Ganguly, A.R. (eds) Knowledge Discovery from Sensor Data. Sensor-KDD 2008. Lecture Notes in Computer Science, vol 5840. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12519-5_8
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DOI: https://doi.org/10.1007/978-3-642-12519-5_8
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