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<title>Deep Reinforcement Learning for the Management of Software-Defined Networks and Network Function Virtualization in an Edge-IoT Architecture</title>
<creator>Alonso Rincón, Ricardo S.</creator>
<creator>Sittón-Candanedo, Inés</creator>
<creator>Casado Vara, Roberto Carlos</creator>
<creator>Prieto, Javier</creator>
<creator>Corchado, Juan M.</creator>
<subject>Industrial internet of things</subject>
<subject>Edge computing</subject>
<subject>Software defined networks</subject>
<subject>Network function virtualization</subject>
<subject>Deep reinforcement learning</subject>
<description>The Internet of Things (IoT) paradigm allows the interconnection of millions of sensor&#xd;
devices gathering information and forwarding to the Cloud, where data is stored and processed&#xd;
to infer knowledge and perform analysis and predictions. Cloud service providers charge users&#xd;
based on the computing and storage resources used in the Cloud. In this regard, Edge Computing&#xd;
can be used to reduce these costs. In Edge Computing scenarios, data is pre-processed and filtered&#xd;
in network edge before being sent to the Cloud, resulting in shorter response times and providing&#xd;
a certain service level even if the link between IoT devices and Cloud is interrupted. Moreover,&#xd;
there is a growing trend to share physical network resources and costs through Network Function&#xd;
Virtualization (NFV) architectures. In this sense, and related to NFV, Software-Defined Networks&#xd;
(SDNs) are used to reconfigure the network dynamically according to the necessities during time.&#xd;
For this purpose, Machine Learning mechanisms, such as Deep Reinforcement Learning techniques,&#xd;
can be employed to manage virtual data flows in networks. In this work, we propose the evolution of&#xd;
an existing Edge-IoT architecture to a new improved version in which SDN/NFV are used over the&#xd;
Edge-IoT capabilities. The proposed new architecture contemplates the use of Deep Reinforcement&#xd;
Learning techniques for the implementation of the SDN controller.</description>
<date>2023-01-17</date>
<date>2023-01-17</date>
<date>2020-07</date>
<type>info:eu-repo/semantics/article</type>
<identifier>http://hdl.handle.net/10259/7245</identifier>
<identifier>10.3390/su12145706</identifier>
<identifier>2071-1050</identifier>
<language>eng</language>
<relation>Sustaunability. 2020, V. 12, n. 14, e5706</relation>
<relation>https://doi.org/10.3390/su12145706</relation>
<relation>info:eu-repo/grantAgreement/EC/POCTEP/0677_DISRUPTIVE_2_E/EU/Intensifying the activity of Digital Innovation Hubs within the PocTep region to boost the development of disruptive and last generation ICTs through cross-border cooperation/</relation>
<rights>http://creativecommons.org/licenses/by/4.0/</rights>
<rights>info:eu-repo/semantics/openAccess</rights>
<rights>Atribución 4.0 Internacional</rights>
<publisher>MDPI</publisher>
</thesis></metadata></record></GetRecord></OAI-PMH>