| Full text | |
| Author(s): |
Casas-Velasco, Daniela M.
;
Villota-Jacome, William F.
;
da Fonseca, Nelson L. S.
;
Rendon, Oscar M. Caicedo
;
IEEE
Total Authors: 5
|
| Document type: | Journal article |
| Source: | 2019 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM); v. N/A, p. 6-pg., 2019-01-01. |
| Abstract | |
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) | |
| FAPESP's process: | 15/24494-8 - Communications and processing of big data in cloud and fog computing |
| Grantee: | Nelson Luis Saldanha da Fonseca |
| Support Opportunities: | Research Projects - Thematic Grants |