@article{sdn-semantic-2019,
author = "Benayas de los Santos, Fernando and Carrera Barroso, {\'A}lvaro and Iglesias, Carlos A. and Garc{\'i}a-Amado, Manuel",
abstract = "Fault Management is a vital issue for any network operator since the beginning of the telecommunications era. As networks have become more and more complex, their management systems are crucial for any operator company. In this ecosystem, the Software‐Defined Networking (SDN) approach has appeared as a possible solution for different networking issues. The flexibility provided by SDN to the network management enables a great dynamism in the configuration of network devices. However, this feature introduces the cost of a potential increase in failures because every modification introduced on the control plane is a new possibility for failures to appear and cause a decrement of the quality for offered services. Because of the growing pace of the networks, the classical approach is not feasible to cope that dynamism. Increasing the number of human operators in charge of the fault management process would increase the operation cost dramatically. Thus, this paper presents an approach to apply machine learning over a big data framework for an autonomous fault management process in SDN networks. In this paper, we present a Semantic Data Lake framework for a self‐diagnosis service, which is deployed on top of an SDN management platform. Moreover, we have developed a prototype of the proposed service with different diagnosis models for SDN networks. Models and algorithms have been evaluated showing good results.",
comments = "JCR 2019 Q3 1.594, SJR 2019 Q2 0.441, Scopus 2019 Q2 3.3",
doi = "https://doi.org/10.1002/ett.3629",
issn = "2161-3915",
journal = "Transactions on Emerging Telecommunications Technologies",
month = "April",
pages = "e3629",
title = "{A} semantic data lake framework for autonomous fault management in {SDN} environments",
url = "https://onlinelibrary.wiley.com/doi/abs/10.1002/ett.3629",
year = "2019",
}