A method for analyzing a content delivery network. The method includes obtaining network traffic flows corresponding to user nodes accessing contents from a set of servers of the content delivery network, extracting a timing attribute from each network traffic flow associated with a server, where the timing attribute is aggregated into a timing attribute dataset of the server based on all network traffic flows associated with the server, generating a statistical measure of the timing attribute dataset as a portion of a feature vector representing the server, where the feature vector is aggregated into a set of feature vectors representing the set of servers, analyzing the set of feature vectors based on a clustering algorithm to generate a set of clusters, and generating, based on the set of clusters, a representation of server groups in the content delivery network.
Unsupervised methodology to unveil content delivery network structures / Giordano, Danilo; Traverso, Stefano; Mellia, Marco; Grimaudo, Luigi; Baralis, ELENA MARIA; Tongaonkar, Alok; Saha, Sabyasachi; Nucci, Antonio. - (2017).
Unsupervised methodology to unveil content delivery network structures
GIORDANO, DANILO;TRAVERSO, STEFANO;MELLIA, Marco;BARALIS, ELENA MARIA;
2017
Abstract
A method for analyzing a content delivery network. The method includes obtaining network traffic flows corresponding to user nodes accessing contents from a set of servers of the content delivery network, extracting a timing attribute from each network traffic flow associated with a server, where the timing attribute is aggregated into a timing attribute dataset of the server based on all network traffic flows associated with the server, generating a statistical measure of the timing attribute dataset as a portion of a feature vector representing the server, where the feature vector is aggregated into a set of feature vectors representing the set of servers, analyzing the set of feature vectors based on a clustering algorithm to generate a set of clusters, and generating, based on the set of clusters, a representation of server groups in the content delivery network.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2675458
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