**7. Conclusions**

The scheduling techniques to resource allocation in distributed systems do not generally meets jointly the user satisfactin and throughput. The QoE emerges as a differentiated paradigm to fill this gap. The QoE approach is particularly important for resource allocation, since the resource allocation adjusted to users needs must to consider contextual parameters. The use of QoC to make the resource allocation allows an efficient management, resulting in a performance gain achieved by a strategic load distribution, improving the level of users QoE. Aiming to act in this mentioned scenario was proposed, and evaluated through experiments, QoC-based approach to provide resource allocation in fog computing. The proposed model performs management decisions based on a QoC policy. The QoC is used also to predict the user's QoE. Our model quantifies resource feasibility considering an application demand. An experiment was conducted to analyze the performance of the proposed model. From the results obtained it was concluded, that our model shows a lower
