Application of Agent Technology for Fault Diagnosis of Telecommunication Networks

Carrera, Álvaro

doi:10.20868/upm.thesis.39170

Cited by 2 publications

(9 citation statements)

References 107 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed agent architecture, named the Bayesian Argumentative Agent , extends the agent architecture presented in a previous works [24,25], labelled as Bayesian Agent in the figure. As the Bayesian Argumentation Framework and the Coordination Protocol are detailed in Section 4 and Section 5, respectively, the following paragraphs presents a brief summary of the Bayesian Agent architecture.…”

Section: Multi-agent Architecture For Distributed Fault Diagnosismentioning

confidence: 99%

“…Thus, the main contributions of the presented work are (i) an argumentation framework for fault diagnosis based on Bayesian reasoning and (ii) a coordination protocol to apply that framework in a Multi-Agent System (MAS) for distributed fault diagnosis in federated network domains. This work was based on Chapter 4 of the first author’s Ph.D. thesis [24].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Bayesian Argumentation Framework for Distributed Fault Diagnosis in Telecommunication Networks

Carrera

Alonso

Iglesias

2019

Sensors

View full text Add to dashboard Cite

Traditionally, fault diagnosis in telecommunication network management is carried out by humans who use software support systems. The phenomenal growth in telecommunication networks has nonetheless triggered the interest in more autonomous approaches, capable of coping with emergent challenges such as the need to diagnose faults’ root causes under uncertainty in geographically-distributed environments, with restrictions on data privacy. In this paper, we present a framework for distributed fault diagnosis under uncertainty based on an argumentative framework for multi-agent systems. In our approach, agents collaborate to reach conclusions by arguing in unpredictable scenarios. The observations collected from the network are used to infer possible fault root causes using Bayesian networks as causal models for the diagnosis process. Hypotheses about those fault root causes are discussed by agents in an argumentative dialogue to achieve a reliable conclusion. During that dialogue, agents handle the uncertainty of the diagnosis process, taking care of keeping data privacy among them. The proposed approach is compared against existing alternatives using benchmark multi-domain datasets. Moreover, we include data collected from a previous fault diagnosis system running in a telecommunication network for one and a half years. Results show that the proposed approach is suitable for the motivational scenario.

show abstract

Section: Multi-agent Architecture For Distributed Fault Diagnosismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Bayesian Argumentation Framework for Distributed Fault Diagnosis in Telecommunication Networks

Carrera

Alonso

Iglesias

2019

Sensors

View full text Add to dashboard Cite

show abstract

“…In the designing of fault diagnosis task, we have followed the B2D2 Diagnosis Model (further explained in Section 4.1), which proposes the division of diagnosis process into three phases: Symptom Detection , Hypothesis Generation , and Hypothesis Discrimination .…”

Section: Semantic Data Lake Architecture For Fault Managementmentioning

confidence: 99%

“…36 The Diagnosis Model combines Structural and Causal Models to allow an agent to autonomously handle that knowledge during the different phases of the diagnosis process defined by the Task Model. In this paper, we propose an extension of the Diagnosis Model 30 for our SDN scenario in Section 4.3.…”

Section: An Overview Of the B2d2 Knowledge Modelmentioning

confidence: 99%

“…Our proposal focuses more on the network behavior than in the topology itself to complement this approach, as described later. We extend a previous approach for Fault Diagnosis of non-SDN Telecommunication Networks, 30 which combines the application of intelligent techniques with semantic modeling. 31 In this paper, we present an extension for the models used in the previous approach to extend them for an SDN environment.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

Benayas

Carrera

García-Amado

et al. 2019

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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.

show abstract

Application of Agent Technology for Fault Diagnosis of Telecommunication Networks

Cited by 2 publications

References 107 publications

A Bayesian Argumentation Framework for Distributed Fault Diagnosis in Telecommunication Networks

A Bayesian Argumentation Framework for Distributed Fault Diagnosis in Telecommunication Networks

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

Contact Info

Product

Resources

About