Delay Estimation in Fogs based on Software-Defined Networking

Autor(es)

Casas-Velasco, Daniela M. ; Villota-Jacome, William F. ; da Fonseca, Nelson L. S. ; Rendon, Oscar M. Caicedo ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2019 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM); v. N/A, p. 6-pg., 2019-01-01.

Resumo

Fog computing brings the advantages and power of cloud computing to the edge of the network. Software-Defined Networking (SDN) has been considered as a feasible solution to cope with the complexity of the orchestration of fog devices. Nevertheless, the use of an SDN controller introduces delays into the transport of packet flows in the fog layer, which may impact on the Quality of Service (QoS) of applications in the continuum IoT-Fog-Cloud. In this paper, we propose a regression model for predicting delay values in an SDN-based fog layer. To build up the regression model, we constructed a dataset, performed data cleaning, carried out feature selection, and applied different Machine Learning (ML) techniques. Our evaluation results reveal that the Random Forest (RF) technique overperforms Decision Tree (DT) and Neural Network (NN) techniques on predicting the delay in an SDN-based fog layer. Furthermore, the predicted delay values reinforce that a fog layer based on SDN can support different latency-sensitive applications. (AU)

• Processo FAPESP: 15/24494-8 - Comunicação e processamento de big data em nuvens e névoas computacionais
• Beneficiário: Nelson Luis Saldanha da Fonseca
• Modalidade de apoio: Auxílio à Pesquisa - Temático