In fog computing, processing, network, and storage resources are placed close to the end users to assure a low latency in comparison to the latency experienced when accessing services in the cloud. One limitation of this solution, however, is that fog nodes are usually fixed, whereas demands are variable over time at all locations, resulting in underutilization of the fog resources as well as unnecessary provisioning of fog resources. One way for dealing with this problem is the employment of mobile nodes to cope with the variability in resource demand.This paper studies how unmanned aerial vehicles (UAVs) equipped with processing capabilities can be used in this perspective, and proposes a solution to the fog node location problem considering both fixed and mobile nodes.It proposes the UAV Fog Node Location (UFL) algorithm to evaluate potential replacements of fixed servers by UAVs. The proposed algorithm can be used for long term planning under the assumption of changes in the prices of UAVs. An evaluation of the problem using data generated by real mobile users shows that UAVs can improve the design of future fog networks.

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Traditional routing protocols employ limited information to make routing decisions which leads to slow adaptation to traffic variability and restricted support to the quality of service requirements of the applications. To address these shortcomings, in previous work, we proposed RSIR, a routing solution based on Reinforcement Learning (RL) in Software-Defined Networking (SDN). However, RL-based solutions usually suffer an increase in the learning process when dealing with large action and state spaces. This paper introduces a different routing approach called Deep Reinforcement Learning and Software-Defined Networking Intelligent Routing (DRSIR). DRSIR defines a routing algorithm based on Deep RL (DRL) in SDN that overcomes the limitations of RL-based solutions. DRSIR considers path-state metrics to produce proactive, efficient, and intelligent routing that adapts to dynamic traffic changes. DRSIR was evaluated by emulation using real and synthetic traffic matrices. The results show that this solution outperforms the routing algorithms based on the Dijkstra's algorithm and RSIR, in relation to stretching (stretch), packet loss, and delay. Moreover, the results obtained demonstrate that DRSIR provides a practical and viable solution for routing in SDN.