The Internet of Things (IoT) paradigm allows the interconnection of millions of sensor
devices gathering information and forwarding to the Cloud, where data is stored and processed
to infer knowledge and perform analysis and predictions. Cloud service providers charge users
based on the computing and storage resources used in the Cloud. In this regard, Edge Computing
can be used to reduce these costs. In Edge Computing scenarios, data is pre-processed and filtered
in network edge before being sent to the Cloud, resulting in shorter response times and providing
a certain service level even if the link between IoT devices and Cloud is interrupted. Moreover,
there is a growing trend to share physical network resources and costs through Network Function
Virtualization (NFV) architectures. In this sense, and related to NFV, Software-Defined Networks
(SDNs) are used to reconfigure the network dynamically according to the necessities during time.
For this purpose, Machine Learning mechanisms, such as Deep Reinforcement Learning techniques,
can be employed to manage virtual data flows in networks. In this work, we propose the evolution of
an existing Edge-IoT architecture to a new improved version in which SDN/NFV are used over the
Edge-IoT capabilities. The proposed new architecture contemplates the use of Deep Reinforcement
Learning techniques for the implementation of the SDN controller.
