(Created page with " == Abstract == The vision of self-driving networks integrates network measurements with network control. Processing data for each of the tasks comprising network control sep...") |
m (Scipediacontent moved page Draft Content 490286147 to Zerwas et al 2018a) |
(No difference)
| |
Latest revision as of 00:18, 29 January 2021
Abstract
The vision of self-driving networks integrates network measurements with network control. Processing data for each of the tasks comprising network control separately might be prohibitive due to the large volume and waste of computational resources. In this work we make the case of using the Weighted Stochastic Block Model (WSBM), a probabilistic model, to learn a task independent representation. In particular, we consider a case study of real-world IP-to-IP communication. The learned representation provides higher level-features for traffic engineering, anomaly detection, or other tasks, and reduces their computational effort. We find that the WSBM is able to accurately model traffic and structure of communication in the considered trace.
Original document
The different versions of the original document can be found in:
- http://dx.doi.org/10.1145/3234200.3234245 under the license http://www.acm.org/publications/policies/copyright_policy#Background
Document information
Published on 01/01/2018
Volume 2018, 2018
DOI: 10.1145/3234200.3234245
Licence: Other
Share this document
Keywords
claim authorship
Are you one of the authors of this document?