Matches in SemOpenAlex for { <https://semopenalex.org/work/W3118998737> ?p ?o ?g. }
Showing items 1 to 69 of
69
with 100 items per page.
- W3118998737 abstract "The network slicing concept promises significant flexibility and autonomy for network management. Thanks to its main key features, heavily relying on the NFV and the SDN technologies, new communication services can be designed and deployed much faster than before. However, maintaining the necessary reliability level of conventional networks remains a major open problem. One of its consequences is that the monitoring of the network infrastructure dedicated to this class of services is an essential challenge, which we address in this thesis through tomography methods. Internet tomography studies the inference of the internal network performances from end-to-end measurements. We study, among other things, its application in the context of network slicing and virtual networks. First, we present the Evolutionary Sampling Algorithm (ESA) as a tool for the inference of hidden nodes' or links' metrics from end-to-end measurements, following a genetic algorithm approach. It enhances the accuracy and the computing time of existing solutions based on the Expectation Maximization (EM) algorithm. Then, we use a trained neural network architecture as an inference tool to deal with the same problem. The model is trained with only a few simulated data. The training time is very short which gives the possibility to repeat it according to the network updates. Second, we addressed the monitors' placement problem. We have studied a particular case where only cycle paths are used. The motivation behind this choice is to avoid the constraints related to synchronization between nodes. The network programmability offered by SDN allows the deployment of such customized type of probing paths. This task is formulated as a set covering problem and we proposed two approaches to solve it, an optimal algorithm and a heuristic one. Finally, we studied the anomaly detection problem in NFV networks. We apply the principles of Boolean network tomography, where we consider that each node can have only two states: up or down. We give necessary and sufficient conditions about the network topology plus a strategy for building the probing paths that guarantee the localization of a fixed maximum number of simultaneously failed nodes." @default.
- W3118998737 created "2021-01-18" @default.
- W3118998737 creator A5089063398 @default.
- W3118998737 date "2020-12-17" @default.
- W3118998737 modified "2023-09-23" @default.
- W3118998737 title "SDN and NFV networks: A new boost and opportunity for network tomography" @default.
- W3118998737 hasPublicationYear "2020" @default.
- W3118998737 type Work @default.
- W3118998737 sameAs 3118998737 @default.
- W3118998737 citedByCount "0" @default.
- W3118998737 crossrefType "dissertation" @default.
- W3118998737 hasAuthorship W3118998737A5089063398 @default.
- W3118998737 hasConcept C105795698 @default.
- W3118998737 hasConcept C119857082 @default.
- W3118998737 hasConcept C120314980 @default.
- W3118998737 hasConcept C124101348 @default.
- W3118998737 hasConcept C146368544 @default.
- W3118998737 hasConcept C151730666 @default.
- W3118998737 hasConcept C154945302 @default.
- W3118998737 hasConcept C2776214188 @default.
- W3118998737 hasConcept C2779343474 @default.
- W3118998737 hasConcept C2780598303 @default.
- W3118998737 hasConcept C31258907 @default.
- W3118998737 hasConcept C33923547 @default.
- W3118998737 hasConcept C41008148 @default.
- W3118998737 hasConcept C81877898 @default.
- W3118998737 hasConcept C86803240 @default.
- W3118998737 hasConceptScore W3118998737C105795698 @default.
- W3118998737 hasConceptScore W3118998737C119857082 @default.
- W3118998737 hasConceptScore W3118998737C120314980 @default.
- W3118998737 hasConceptScore W3118998737C124101348 @default.
- W3118998737 hasConceptScore W3118998737C146368544 @default.
- W3118998737 hasConceptScore W3118998737C151730666 @default.
- W3118998737 hasConceptScore W3118998737C154945302 @default.
- W3118998737 hasConceptScore W3118998737C2776214188 @default.
- W3118998737 hasConceptScore W3118998737C2779343474 @default.
- W3118998737 hasConceptScore W3118998737C2780598303 @default.
- W3118998737 hasConceptScore W3118998737C31258907 @default.
- W3118998737 hasConceptScore W3118998737C33923547 @default.
- W3118998737 hasConceptScore W3118998737C41008148 @default.
- W3118998737 hasConceptScore W3118998737C81877898 @default.
- W3118998737 hasConceptScore W3118998737C86803240 @default.
- W3118998737 hasLocation W31189987371 @default.
- W3118998737 hasOpenAccess W3118998737 @default.
- W3118998737 hasPrimaryLocation W31189987371 @default.
- W3118998737 hasRelatedWork W2167669776 @default.
- W3118998737 hasRelatedWork W2182887170 @default.
- W3118998737 hasRelatedWork W2196263691 @default.
- W3118998737 hasRelatedWork W2470741238 @default.
- W3118998737 hasRelatedWork W2536349590 @default.
- W3118998737 hasRelatedWork W2577729392 @default.
- W3118998737 hasRelatedWork W2754446034 @default.
- W3118998737 hasRelatedWork W2769861256 @default.
- W3118998737 hasRelatedWork W2808218114 @default.
- W3118998737 hasRelatedWork W2845252065 @default.
- W3118998737 hasRelatedWork W2856479366 @default.
- W3118998737 hasRelatedWork W288065512 @default.
- W3118998737 hasRelatedWork W2887525730 @default.
- W3118998737 hasRelatedWork W2909519685 @default.
- W3118998737 hasRelatedWork W2947790157 @default.
- W3118998737 hasRelatedWork W3001015008 @default.
- W3118998737 hasRelatedWork W3046623339 @default.
- W3118998737 hasRelatedWork W3093710492 @default.
- W3118998737 hasRelatedWork W3107756477 @default.
- W3118998737 hasRelatedWork W3157138167 @default.
- W3118998737 isParatext "false" @default.
- W3118998737 isRetracted "false" @default.
- W3118998737 magId "3118998737" @default.
- W3118998737 workType "dissertation" @default.