2019
DOI: 10.1002/ett.3643
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Adapting reinforcement learning for multimedia transmission on SDN

Abstract: Multimedia transmissions require a high quantity of resources to ensure their quality. In the last years, some technologies that provide a better resource management have appeared. Software‐defined networks (SDNs) are presented as a solution to improve this management. Furthermore, by combining an SDN with artificial intelligence (AI) techniques, networks are able to provide a higher performance using the same resources. In this paper, a redefinition of reinforcement learning is proposed. This model is focused… Show more

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Cited by 3 publications
(1 citation statement)
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“…The following four papers deal with the application of ML in SDN and network virtualization. Benayas et al presented autonomous SDN fault management platform that uses Bayesian diagnosis in the seventh paper, “A semantic data lake framework for autonomous fault management in SDN environments.” Article 8, “Adapting reinforcement learning for multimedia transmission on SDN,” by Rego et al, enhances decisions of OpenFlow and guarantees QoS by applying ML on multimedia transmissions of SDN. The ninth paper, by Zangiabady et al, “Self‐adaptive online virtual network migration in network virtualization environments,” leverages reinforcement learning to minimize the cost associated with migrating virtual network resource from one to another.…”
mentioning
confidence: 99%
“…The following four papers deal with the application of ML in SDN and network virtualization. Benayas et al presented autonomous SDN fault management platform that uses Bayesian diagnosis in the seventh paper, “A semantic data lake framework for autonomous fault management in SDN environments.” Article 8, “Adapting reinforcement learning for multimedia transmission on SDN,” by Rego et al, enhances decisions of OpenFlow and guarantees QoS by applying ML on multimedia transmissions of SDN. The ninth paper, by Zangiabady et al, “Self‐adaptive online virtual network migration in network virtualization environments,” leverages reinforcement learning to minimize the cost associated with migrating virtual network resource from one to another.…”
mentioning
confidence: 99%