Robustness against large-scale failures in communications networks
This thesis studies robustness against large-scale failures in communications networks. If failures are isolated, they usually go unnoticed by users thanks to recovery mechanisms. However, such mechanisms are not effective against large-scale multiple failures. Large-scale failures may cause huge ec...
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| Format: | doctoral thesis |
| Status: | Published version |
| Publication Date: | 2011 |
| Country: | España |
| Institution: | CBUC, CESCA |
| Repository: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/70008 |
| Online Access: | http://hdl.handle.net/10803/70008 |
| Access Level: | Open access |
| Keyword: | Robustness Robustez Resilience Resistència Resistencia Complex networks Xarxes complexes Redes complejas GMPLS Network recovery Recuperació de la xarxa Recuperación de la red Epidemic models Models epidèmics Modelos epidémicos 68 |
| Summary: | This thesis studies robustness against large-scale failures in communications networks. If failures are isolated, they usually go unnoticed by users thanks to recovery mechanisms. However, such mechanisms are not effective against large-scale multiple failures. Large-scale failures may cause huge economic loss. A key requirement towards devising mechanisms to lessen their impact is the ability to evaluate network robustness. This thesis focuses on multilayer networks featuring separated control and data planes. The majority of the existing measures of robustness are unable to capture the true service degradation in such a setting, because they rely on purely topological features. One of the major contributions of this thesis is a new measure of functional robustness. The failure dynamics is modeled from the perspective of epidemic spreading, for which a new epidemic model is proposed. Another contribution is a taxonomy of multiple, large-scale failures, adapted to the needs and usage of the field of networking. |
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