2020
DOI: 10.1016/j.comcom.2019.12.005
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User mobility and Quality-of-Experience aware placement of Virtual Network Functions in 5G

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Cited by 28 publications
(30 citation statements)
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“…Moreover, the lack of considering the recomputation of VNF placements in these methods makes them ill-equipped to be employed in practical settings, and often the consequences are violations of the Quality of Service (QoS) and Service Level Agreement (SLA) [10]. Ongoing researches evolve around different optimization formulations using Integer Linear Programming (ILP) and Mixed Integer Linear Programming (MILP) to outline the VNF orchestration scheme, which is NP-hard by nature [11] [12], and fail to offer fast VNF placements decisions at different times. To approach intelligent VNF orchestration, meta-heuristic based swarm intelligence algorithm, specifically, Ant Colony Optimization (ACO), has been proposed in [12].…”
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confidence: 99%
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“…Moreover, the lack of considering the recomputation of VNF placements in these methods makes them ill-equipped to be employed in practical settings, and often the consequences are violations of the Quality of Service (QoS) and Service Level Agreement (SLA) [10]. Ongoing researches evolve around different optimization formulations using Integer Linear Programming (ILP) and Mixed Integer Linear Programming (MILP) to outline the VNF orchestration scheme, which is NP-hard by nature [11] [12], and fail to offer fast VNF placements decisions at different times. To approach intelligent VNF orchestration, meta-heuristic based swarm intelligence algorithm, specifically, Ant Colony Optimization (ACO), has been proposed in [12].…”
mentioning
confidence: 99%
“…Ongoing researches evolve around different optimization formulations using Integer Linear Programming (ILP) and Mixed Integer Linear Programming (MILP) to outline the VNF orchestration scheme, which is NP-hard by nature [11] [12], and fail to offer fast VNF placements decisions at different times. To approach intelligent VNF orchestration, meta-heuristic based swarm intelligence algorithm, specifically, Ant Colony Optimization (ACO), has been proposed in [12]. However, the family of swarm intelligence algorithms, including ACO, requires extensive parameter tuning of exploitation-exploration ratios, making them heavy weighted to accommodate for real-time orchestration [12].…”
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confidence: 99%
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