Towards an autonomic Bayesian fault diagnosis service for SDN environments based on a big data infrastructure

Benayas, Fernando; Carrera, Álvaro; Iglesias, Carlos Á.

doi:10.1109/sds.2018.8370415

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…The prototype developed for experimentation purposes has been evaluated in the same testbed as the previous work . We have included another machine learning algorithm, which includes fast‐model learning .…”

Section: Discussionmentioning

confidence: 99%

“…The prototype developed for experimentation purposes has been evaluated in the same testbed as the previous work. 10 We have included another machine learning algorithm, which includes fast-model learning. 39 Then, we evaluate the quality of two different models for switch faults using two different algorithms.…”

Section: Discussionmentioning

confidence: 99%

“…A previous work of the architecture presented in this paper was introduced at the 2018 Fifth International Conference on Software Defined Systems. 10 In this paper, we include a semantic layer to enable the autonomous diagnosis process by an intelligent agent. We have developed several ontologies for the data description and the diagnosis process of our SDN scenario.…”

Section: Introductionmentioning

confidence: 99%

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A semantic data lake framework for autonomous fault management in SDN environments

Benayas

Carrera

García-Amado

et al. 2019

Trans Emerging Tel Tech

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A semantic data lake framework for autonomous fault management in SDN environments

Benayas

Carrera

García-Amado

et al. 2019

Trans Emerging Tel Tech

Self Cite

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“…Failure prediction has become a reality thanks to the introduction of ML techniques. Benayas et al [110] presented an architecture for a self-diagnosis service with ML and data analysis. In addition, it is encouraging that a prototype with different diagnosis models for SDN has been developed, which will be explored in future.…”

Section: ) Network Fault Diagnosismentioning

confidence: 99%

A Survey of Networking Applications Applying the Software Defined Networking Concept Based on Machine Learning

et al. 2019

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The main task of future networks is to build, as much as possible, intelligent networking architectures for intellectualization, activation, and customization. Software-defined networking (SDN) technology breaks the tight coupling between the control plane and the data plane in the traditional network architecture, making the controllability, security, and economy of network resources into a reality. As one of the important actualization methods of artificial intelligence (AI), machine learning (ML), combined with SDN architecture will have great potential in areas, such as network resource management, route planning, traffic scheduling, fault diagnosis, and network security. This paper presents the network applications combined with SDN concepts based on ML from two perspectives, namely the perspective of ML algorithms and SDN network applications. From the perspective of ML algorithms, this paper focuses on the applications of classical ML algorithms in SDN-based networks, after a characteristic analysis of algorithms. From the other perspective, after classifying the existing network applications based on the SDN architecture, the related ML solutions are introduced. Finally, the future development of the ML algorithms and SDN concepts is discussed and analyzed. This paper occupies the intersection of the AI, big data, computer networking, and other disciplines; the AI itself is a new and complex interdisciplinary field, which causes the researchers in this field to often have different professional backgrounds and, sometimes, divergent research purposes. This paper is necessary and helpful for researchers from different fields to accurately master the key issues.INDEX TERMS Artificial intelligence, machine learning, network management, software-defined networking.

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Big Data-Based Precise Diagnosis in Space Range Communication Systems

Gao

Ao²,

Zhou³

et al. 2019

Lecture Notes in Electrical Engineering

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Towards an autonomic Bayesian fault diagnosis service for SDN environments based on a big data infrastructure

Cited by 7 publications

References 14 publications

A semantic data lake framework for autonomous fault management in SDN environments

A semantic data lake framework for autonomous fault management in SDN environments

A Survey of Networking Applications Applying the Software Defined Networking Concept Based on Machine Learning

Big Data-Based Precise Diagnosis in Space Range Communication Systems

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