Topology and Energy Aware Approximate Algorithm for QoS-Based Resource Slicing in 5G Core Networks

This paper proposes a multi-objective optimization framework for network function virtualization (NFV) resource assignment in 5G core networks, targeting the minimization of both resource cost and server energy consumption subject to stringent quality of service (QoS) constraints for diverse service...

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Vydáno v:IEEE access Ročník 13; s. 176885 - 176900
Hlavní autoři: Sargolzaei, Ehsan, Rasti, Mehdi, Khorsandi, Siavash
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
5G mobile communication
Algorithms
Approximation algorithms
Bandwidths
Central processing units
Constraints
Costs
CPUs
Delays
Energy consumption
Energy costs
Heuristic algorithms
Mixed integer
Multiple objective analysis
Network function virtualization
network slicing
Nonlinear programming
Optimization
Quality of service
resource assignment
Resource management
Servers
Statistical analysis
Topology
virtual network functions
Virtual networks
ISSN:2169-3536, 2169-3536
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Shrnutí:This paper proposes a multi-objective optimization framework for network function virtualization (NFV) resource assignment in 5G core networks, targeting the minimization of both resource cost and server energy consumption subject to stringent quality of service (QoS) constraints for diverse services. This problem considers the assignment of CPU and bandwidth resources to servers and links, subject to network resource constraints. Given the dynamic nature of 5G core networks, where bandwidth and CPU processing demands fluctuate due to time-varying demand, user mobility, diverse QoS requirements, potential link failures, server outages, and the coexistence of multiple virtual networks (VNs), a dynamic resource assignment strategy is adopted in this work as opposed to static approaches in previous works. The majorization-minimization approach is used to handle the resulting mixed integer nonlinear programming problem, and an approximation-based algorithm for core network resource assignment (ACNRA) with an affordable computational complexity is proposed. The simulation results demonstrate the effectiveness of the proposed algorithm in reducing both network resource costs and server energy consumption. Statistical analysis of these results shows that our proposed method outperforms benchmark algorithms, achieving average reductions of 5.92% and 10.28% in resource cost, and 5.43% and 10.10% in energy consumption.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2025.3616851