ICC 2019 - 2019 IEEE International Conference on Communications (ICC) 2019
DOI: 10.1109/icc.2019.8761633
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A Delay-Aware Deployment Policy for End-to-End 5G Network Slicing

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Cited by 5 publications
(2 citation statements)
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“…Ricart-Sanchz et al [32] proposed a QoS-aware NS framework based on hardware acceleration to support data plane programmability [32]. Another important issue is resource deployment, and management policy for E2E NS [33,34]. Solutions proposed in [10,35] leveraged edge/fog computing for optimizing the E2E NS.…”
Section: Qos Support For Reliable Nsmentioning
confidence: 99%
“…Ricart-Sanchz et al [32] proposed a QoS-aware NS framework based on hardware acceleration to support data plane programmability [32]. Another important issue is resource deployment, and management policy for E2E NS [33,34]. Solutions proposed in [10,35] leveraged edge/fog computing for optimizing the E2E NS.…”
Section: Qos Support For Reliable Nsmentioning
confidence: 99%
“…Numerous approaches (e.g., [18]- [23], among others) based on network embedding have also been published in recent years. In particular, the authors in [18] proposed a virtual network embedding problem based on 3-D resources that includes computing, network, and storage; [19] looked into a network slice embedding problem that considers the deployment costs due to the slice's minimum resource requirements (both at the nodes and links), as well as a cost related to the end-to-end delay (i.e., propagation and processing delays plus the virtualization overhead); [20] proposed an efficient heuristic for network slice embedding that allocates the network slice resources based on node rankings (four possible ranking algorithms are evaluated); and [21] proposed an algorithm for automatic virtual network embedding based on deep reinforcement learning with a novel multi-objective reward function. Meanwhile, [22] and [23] investigated the partitioning of multi-domain virtual networks and proposed heuristics based on particle swarm optimization; the latter also proposed a two-step embedding strategy for the inter-and intradomain embedding sub-problems.…”
Section: Related Workmentioning
confidence: 99%