A Systematic Survey of Slice-Resource Optimization in the 5G Core with New Insights from 2-Edge-Connected Subgraph Models

Igor Begić; Zoran Injić

doi:https://doi.org/10.47960/3029-3200.2025.1.2.37

Review Article

DOI: https://doi.org/10.47960/3029-3200.2025.1.2.37

Citations (Crossref, OpenAlex): …

Igor Begić*

and Zoran Injić

*CA	igor.begic@fsre.sum.ba
	zoran.injic@fsre.sum.ba

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Vol. 1 No. 2 Article 9

This article belongs to Vol. 1 No. 2, 2025

I. Begić and Z. Injić, “A Systematic Survey of Slice-Resource Optimization in the 5G Core with New Insights from 2-Edge-Connected Subgraph Models,” International Journal of Innovative Solutions in Engineering, vol. 1, no. 2, pp. 37–46, Jul. 2025, doi: 10.47960/3029-3200.2025.1.2.37.

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Abstract

Network slicing now delivers enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC) over a common 5G core, yet deciding which slice requests to admit, where to place each virtual network function (VNF), and how to route and protect traffic forms an NP-hard mixed-integer problem. We first cast those decisions in a compact 0–1/mixed-integer programme whose feasible set Ω unifies admission, placement, latency, and capacity constraints. That formulation provides a common yardstick for four solution lines reviewed here: (i) exact models solved by integer linear programming (ILP); (ii) fast constructive heuristics; (iii) evolutionary and neighbourhood-search meta-heuristics; and (iv) learning-driven approaches built on deep- and graph-reinforcement learning (RL). Evidence from more than thirty benchmark studies reveals a clear pattern: ILP remains optimal up to roughly fifty nodes but scales poorly; greedy or multi-stage heuristics retain 85–95% of ILP revenue while running in near-linear time; well-tuned meta-heuristics cut the gap to under eight per cent on thousand-node cores; and trained RL agents issue millisecond decisions only a few points shy of meta-heuristic quality. Two recent two-edge-connected embedding schemes are included as illustrative case studies. Open challenges—hybrid matheuristic workflows, energy-aware objectives, reproducible failure-rich benchmarks, and sub-millisecond control-plane inference—are outlined as priorities for dependable slice management in 5G and beyond.

Keywords

5G Core Network, Network Slicing, Integer-Linear Programming, Evolutionary Meta-Heuristics, Deep-Reinforcement Learning, Two-Edge-Connected Protection

ijise ID

9

URI

https://ijise.ba/article/9/

Publication Date

Jul. 17, 2025

References

M. Vincenzi, E. Lopez-Aguilera, and E. Garcia-Villegas, “Maximizing Infrastructure Providers’ Revenue Through Network Slicing in 5G,” IEEE Access, vol. 7, pp. 128283–128297, 2019, doi: https://doi.org/10.1109/ACCESS.2019.2939935.
B. Han, S. Tayade, and H. D. Schotten, “Modeling profit of sliced 5G networks for advanced network resource management and slice implementation,” in 2017 IEEE Symposium on Computers and Communications (ISCC), IEEE, Jul. 2017, pp. 576–581. doi: https://doi.org/10.1109/ISCC.2017.8024590.
M. A. Madani, F. Zhou, and A. Meddahi, “Adaptive ILP Formulation for Disaster-Resilient Service Function Chains in Beyond 5G Networks,” in 2023 IEEE Conference on Standards for Communications and Networking (CSCN), IEEE, Nov. 2023, pp. 346–352. doi: https://doi.org/10.1109/CSCN60443.2023.10453128.
Z. Xiong, Y. Zhang, D. Niyato, R. Deng, P. Wang, and L.-C. Wang, “Deep Reinforcement Learning for Mobile 5G and Beyond: Fundamentals, Applications, and Challenges,” IEEE Vehicular Technology Magazine, vol. 14, no. 2, pp. 44–52, Jun. 2019, doi: https://doi.org/10.1109/MVT.2019.2903655.
M. Laha and R. Datta, “A Steiner Tree based efficient network infrastructure design in 5G urban vehicular networks,” Comput Commun, vol. 201, pp. 59–71, Mar. 2023, doi: https://doi.org/10.1016/j.comcom.2023.01.016.
A. Satja Kurdija, Ž. Ilić, and I. Begić, “A Framework for 5G Network Slicing Optimization using 2-Edge-Connected Subgraphs for Path Protection,” International journal of electrical and computer engineering systems, vol. 15, no. 8, pp. 675–685, Sep. 2024, doi: https://doi.org/10.32985/ijeces.15.8.5.
I. Begić, A. S. Kurdija, and J. Matuško, “A Framework for 5G Network Slicing Optimization with Path Protection,” in 2024 47th MIPRO ICT and Electronics Convention (MIPRO), IEEE, May 2024, pp. 742–747. doi: https://doi.org/10.1109/MIPRO60963.2024.10569740.
D. Irawan, N. R. Syambas, A. A. N. Ananda Kusuma, and E. Mulyana, “Network Slicing Algorithms Case Study:Virtual Network Embedding,” in 2020 14th International Conference on Telecommunication Systems, Services, and Applications (TSSA, IEEE, Nov. 2020, pp. 1–5. doi: https://doi.org/10.1109/TSSA51342.2020.9310856.
M. Ait aba, M. Elkael, B. Jouaber, H. Castel-Taleb, A. Araldo, and D. Olivier, “A two-stage algorithm for the Virtual Network Embedding problem,” in 2021 IEEE 46th Conference on Local Computer Networks (LCN), IEEE, Oct. 2021, pp. 395–398. doi: https://doi.org/10.1109/LCN52139.2021.9524968.
M. K. Singh, S. Vittal, and A. Antony Franklin, “SERENS: Self Regulating Network Slicing in 5G for Efficient Resource Utilization,” in 2020 IEEE 3rd 5G World Forum (5GWF), IEEE, Sep. 2020, pp. 590–595. doi: https://doi.org/10.1109/5GWF49715.2020.9221405.
B. Jaumard and Q. H. Duong, “A Nested Decomposition Model for Reliable NFV 5G Network Slicing,” IEEE Transactions on Network and Service Management, vol. 20, no. 3, pp. 2186–2200, Sep. 2023, doi: https://doi.org/10.1109/TNSM.2023.3303388.
C. Raffaelli, E. Amato, P. Monti, and F. Tonini, “Reliable Slicing in Optical Metro Networks with Reconfigurable Backup Resources,” in 2022 13th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP), IEEE, Jul. 2022, pp. 863–866. doi: https://doi.org/10.1109/CSNDSP54353.2022.9908047.
N. Shahriar et al., “Reliable Slicing of 5G Transport Networks With Bandwidth Squeezing and Multi-Path Provisioning,” IEEE Transactions on Network and Service Management, vol. 17, no. 3, pp. 1418–1431, Sep. 2020, doi: https://doi.org/10.1109/TNSM.2020.2992442.
M. Kyung Lee and C. Seon Hong, “Efficient Slice Allocation for Novel 5G Services,” in 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN), IEEE, Jul. 2018, pp. 625–629. doi: https://doi.org/10.1109/ICUFN.2018.8436824.
Y. Xiao, J. Zhang, P. Zhu, H. Wu, and C. Zhang, “Customized Topology-Level Protection for Reliable Slicing in 5G/B5G Metro Access/Aggregation Networks,” Journal of Lightwave Technology, vol. 42, no. 9, pp. 3068–3080, May 2024, doi: https://doi.org/10.1109/JLT.2024.3356056.
H. Ko, J. Lee, H. Choi, and S. Pack, “Hierarchical Identifier (HID)-based 5G Architecture with Backup Slice,” in 2020 21st Asia-Pacific Network Operations and Management Symposium (APNOMS), IEEE, Sep. 2020, pp. 291–293. doi: https://doi.org/10.23919/APNOMS50412.2020.9236966.
N. Shahriar et al., “Reliable Slicing of 5G Transport Networks with Dedicated Protection,” in 2019 15th International Conference on Network and Service Management (CNSM), IEEE, Oct. 2019, pp. 1–9. doi: https://doi.org/10.23919/CNSM46954.2019.9012711.
V. Balasubramanian, M. Aloqaily, and M. Reisslein, “Mutes: Multi-Tenant Switching for 5G Network Slice Revenue Maximization,” in 2022 International Wireless Communications and Mobile Computing (IWCMC), IEEE, May 2022, pp. 590–595. doi: https://doi.org/10.1109/IWCMC55113.2022.9825277.
A. Sarah and G. Nencioni, “Resource allocation for cost minimization of a slice broker in a 5G-MEC scenario,” Comput Commun, vol. 213, pp. 331–344, Jan. 2024, doi: https://doi.org/10.1016/j.comcom.2023.11.016.
D. Basu, S. Kal, U. Ghosh, and R. Datta, “DRIVE: Dynamic Resource Introspection and VNF Embedding for 5G Using Machine Learning,” IEEE Internet Things J, vol. 10, no. 21, pp. 18971–18979, Nov. 2023, doi: https://doi.org/10.1109/JIOT.2023.3235382.
X. Li and C. Qian, “The virtual network function placement problem,” in 2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE, Apr. 2015, pp. 69–70. doi: https://doi.org/10.1109/INFCOMW.2015.7179347.
O. Soualah, M. Mechtri, C. Ghribi, and D. Zeghlache, “A Green VNF-FG Embedding Algorithm,” in 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft), IEEE, Jun. 2018, pp. 141–149. doi: https://doi.org/10.1109/NETSOFT.2018.8460013.
F. Bari, S. R. Chowdhury, R. Ahmed, R. Boutaba, and O. C. M. B. Duarte, “Orchestrating Virtualized Network Functions,” IEEE Transactions on Network and Service Management, vol. 13, no. 4, pp. 725–739, Dec. 2016, doi: https://doi.org/10.1109/TNSM.2016.2569020.
R. Mijumbi, J. Serrat, J.-L. Gorricho, J. Rubio-Loyola, and S. Davy, “Server placement and assignment in virtualized radio access networks,” in 2015 11th International Conference on Network and Service Management (CNSM), IEEE, Nov. 2015, pp. 398–401. doi: https://doi.org/10.1109/CNSM.2015.7367390.
B. Addis, D. Belabed, M. Bouet, and S. Secci, “Virtual network functions placement and routing optimization,” in 2015 IEEE 4th International Conference on Cloud Networking (CloudNet), IEEE, Oct. 2015, pp. 171–177. doi: https://doi.org/10.1109/CloudNet.2015.7335301.
A. Marotta and A. Kassler, “A Power Efficient and Robust Virtual Network Functions Placement Problem,” in 2016 28th International Teletraffic Congress (ITC 28), IEEE, Sep. 2016, pp. 331–339. doi: https://doi.org/10.1109/ITC-28.2016.151.
S. Mehraghdam, M. Keller, and H. Karl, “Specifying and placing chains of virtual network functions,” in 2014 IEEE 3rd International Conference on Cloud Networking (CloudNet), IEEE, Oct. 2014, pp. 7–13. doi: https://doi.org/10.1109/CloudNet.2014.6968961.
N. Tastevin, M. Obadia, and M. Bouet, “A graph approach to placement of Service Functions Chains,” in 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), IEEE, May 2017, pp. 134–141. doi: https://doi.org/10.23919/INM.2017.7987273.
H. Li, L. Wang, X. Wen, Z. Lu, and J. Li, “MSV: An Algorithm for Coordinated Resource Allocation in Network Function Virtualization,” IEEE Access, vol. 6, pp. 76876–76888, 2018, doi: https://doi.org/10.1109/ACCESS.2018.2882255.
S. Agarwal, F. Malandrino, C. F. Chiasserini, and S. De, “VNF Placement and Resource Allocation for the Support of Vertical Services in 5G Networks,” IEEE/ACM Transactions on Networking, vol. 27, no. 1, pp. 433–446, Feb. 2019, doi: https://doi.org/10.1109/TNET.2018.2890631.
B. Li, B. Cheng, M. Wang, X. Liu, Y. Yue, and J. Chen, “Joint Correlation-Aware VNF Selection and Placement in Cloud Data Center Networks,” in 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS), IEEE, Dec. 2019, pp. 171–176. doi: https://doi.org/10.1109/ICPADS47876.2019.00034.
K. Yang, H. Zhang, and P. Hong, “Energy-Aware Service Function Placement for Service Function Chaining in Data Centers,” in 2016 IEEE Global Communications Conference (GLOBECOM), IEEE, Dec. 2016, pp. 1–6. doi: https://doi.org/10.1109/GLOCOM.2016.7841805.
A. Leivadeas, G. Kesidis, M. Falkner, and I. Lambadaris, “A Graph Partitioning Game Theoretical Approach for the VNF Service Chaining Problem,” IEEE Transactions on Network and Service Management, vol. 14, no. 4, pp. 890–903, Dec. 2017, doi: https://doi.org/10.1109/TNSM.2017.2732699.
S. Kim, S. Park, Y. Kim, S. Kim, and K. Lee, “VNF-EQ: dynamic placement of virtual network functions for energy efficiency and QoS guarantee in NFV,” Cluster Comput, vol. 20, no. 3, pp. 2107–2117, Sep. 2017, doi: https://doi.org/10.1007/s10586-017-1004-3.
S. Khebbache, M. Hadji, and D. Zeghlache, “A multi-objective non-dominated sorting genetic algorithm for VNF chains placement,” in 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC), IEEE, Jan. 2018, pp. 1–4. doi: https://doi.org/10.1109/CCNC.2018.8319250.
M. C. Luizelli, W. L. da Costa Cordeiro, L. S. Buriol, and L. P. Gaspary, “A fix-and-optimize approach for efficient and large scale virtual network function placement and chaining,” Comput Commun, vol. 102, pp. 67–77, Apr. 2017, doi: https://doi.org/10.1016/j.comcom.2016.11.002.
S. Il Kim and H. S. Kim, “A research on dynamic service function chaining based on reinforcement learning using resource usage,” in 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN), IEEE, Jul. 2017, pp. 582–586. doi: https://doi.org/10.1109/ICUFN.2017.7993856.
M. Z. Chowdhury, Md. T. Hossan, and Y. M. Jang, “Applying Model-Free Reinforcement Learning Algorithm in Network Slicing for 5G,” in 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), IEEE, May 2019, pp. 1–4. doi: https://doi.org/10.1109/ICASERT.2019.8934443.
S. Wang and T. Lv, “Deep Reinforcement Learning for Demand-Aware Joint VNF Placement-and-Routing,” in 2019 IEEE Globecom Workshops (GC Wkshps), IEEE, Dec. 2019, pp. 1–6. doi: https://doi.org/10.1109/GCWkshps45667.2019.9024688.
J. Pei, P. Hong, M. Pan, J. Liu, and J. Zhou, “Optimal VNF Placement via Deep Reinforcement Learning in SDN/NFV-Enabled Networks,” IEEE Journal on Selected Areas in Communications, vol. 38, no. 2, pp. 263–278, Feb. 2020, doi: https://doi.org/10.1109/JSAC.2019.2959181.
H. A. Kholidy and R. Kamaludeen, “An Innovative Hashgraph-based Federated Learning Approach for Multi Domain 5G Network Protection,” in 2022 IEEE Future Networks World Forum (FNWF), IEEE, Oct. 2022, pp. 139–146. doi: https://doi.org/10.1109/FNWF55208.2022.00033.