2020 23rd Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) 2020
DOI: 10.1109/icin48450.2020.9059438
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Multi-objective Optimisation for Slice-aware Resource Orchestration in 5G Networks

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Cited by 3 publications
(2 citation statements)
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“…We propose the use of an AI model based on Dilated Causal Convolutional Neural Networks (DCNN) [11] trained via Deep Neuroevolution, to predict the incoming traffic and subsequently use this result as input to the resource allocation process. Deep Neuroevolutional techniques combine two sub-fields of AI, Deep Neural Networks (DNN) and Evolutionary Algorithms [12]. Applying…”
Section: A Prediction Of User Mobilitymentioning
confidence: 99%
“…We propose the use of an AI model based on Dilated Causal Convolutional Neural Networks (DCNN) [11] trained via Deep Neuroevolution, to predict the incoming traffic and subsequently use this result as input to the resource allocation process. Deep Neuroevolutional techniques combine two sub-fields of AI, Deep Neural Networks (DNN) and Evolutionary Algorithms [12]. Applying…”
Section: A Prediction Of User Mobilitymentioning
confidence: 99%
“…This is an effort to satisfy different requirements regarding the bandwidth, computing, and end-to-end latency constraints. Likewise, a multi-objective technique to realize an optimized resource orchestration among cloud-based slices was presented in [53]. Moreover, in [54], a comprehensive survey on software-defined optical networks (SDONs) was presented.…”
Section: Enabling Technologiesmentioning
confidence: 99%