PREDICTIVE MULTI-LAYER RESOURCE SLICING FOR 5G NETWORKS
DOI:
https://doi.org/10.15588/1607-3274-2026-2-2Keywords:
carbon-aware networking, multi-layer slicing, 5G/6G, predictive orchestration, bursty traffic, M/G/1 queueing, edge-cloud, sustainable networkingAbstract
Context. The rapid deployment of 5G networks and the emergence of 6G architectures introduce unprecedented traffic heterogeneity and burstiness across radio, edge, and core domains. Meanwhile, the energy footprint of mobile infrastructure is becoming a major sustainability concern, as carbon emissions increasingly shape network operation policies.
Objective. This work aims to design a predictive carbon-aware multi-layer resource slicing framework for RAN – edge – core 5G/6G networks that jointly optimizes latency, cost, energy, and carbon emissions under bursty traffic conditions.
Method. The proposed approach integrates an M/G/1-based queuing model for accurate representation of heavy-tailed service times and bursty arrival patterns; hybrid short-term/long-term forecasting of both traffic load and regional carbon intensity; and multi-objective optimization for carbon-aware VNF placement and traffic steering across network layers. A proactive – reactive orchestration mechanism performs predictive resource pre-allocation and runtime scaling.
Results. Trace-driven simulations on a representative multi-layer testbed demonstrate a 34% reduction in CO2 emissions compared to latency-first orchestration, alongside a 22% decrease in operational cost and <1% SLA violation rate. Tail latency remains within slice-specific thresholds even under bursty loads, confirming that carbon reductions can be achieved without service degradation.
Conclusions. Predictive, carbon-aware orchestration across RAN-edge-core domains substantially improves environmental and economic efficiency while preserving QoS guarantees. The results highlight the importance of integrating forecast-driven
optimization and realistic traffic modeling into next-generation slicing architectures.
References
Globa L., Sulima S., Skulysh M., Dovgyi S., Stryzhak O. Architecture and Operation Algorithms of Mobile Core Network with Virtualization. IntechOpen, 2020, 21 p. DOI: 10.5772/intechopen.89608
Yaqoob M., Trestian R., Tatipamula M., Nguyen H. X. Digital-Twin-Driven End-to-End Network Slicing Toward 6G. IEEE Internet Computing, 2024, Vol. 28, No. 2, pp. 47–55. DOI: 10.1109/MIC.2023.3332252
How 5G Network Slicing Expands Opportunities [Electronic resource]. IPLOOK. Available at: https://www.iplook.com/info/how-5g-network-slicingexpands-opportunities-i00232i1.html (accessed: 22.10.2025).
Tiwari K. Phulre A. K., Vishnu D. Enhancing Secure Key Management Techniques for Optimised 5G Network Slicing Security. Applied Cybersecurity & Internet Governance, 2024, Vol. 3, No. 2, pp. 170–210. DOI: 10.60097/ACIG/199725
Sargolzaei E., Rasti M., Khorsandi S. Topology and Energy Aware Approximate Algorithm for QoS-based Resource Slicing in 5G Core Networks. IEEE Access, 2025, Vol. 13, pp. 176885–176900. DOI: 10.1109/ACCESS.2025.3616851.
Khalili H., Papageorgiou A., Siddiqui S., Meixner C. C., Carrozzo G., Nejabati R., Simeonidou D. Network Slicing-aware NFV Orchestration for 5G Service Platforms. Networks and Communications : European Conference EuCNC-2019, Valencia, 18–22 June, 2019 : proceedings, pp. 25–30.
Kim Y., Lim H. Multi-Agent Reinforcement LearningBased Resource Management for End-to-End Network Slicing. IEEE Access, 2021, Vol. 9, pp. 56178–56190.
Tsai C.-C., Lin F. J., Tanaka H. Evaluation of 5G Core Slicing on User Plane Function. Communications and Network, 2021, Vol. 13, No. 3, pp. 79–92. DOI: 10.4236/cn.2021.133007
Shao Y. Li R., Hu B., Wu Y., Zhao Z., Zhang H. Graph Attention Network-Based Multi-Agent Reinforcement Learning for Slicing Resource Management in Dense Cellular Network. IEEE Transactions on Vehicular Technology, 2021, Vol. 70, pp. 10792–10803.
Tariq M. A., Saad M. M., Ajmal M., Jeon D., Kim J., Kim D. Proactive Resource Management for Seamless Service: A Transition from 5G-Basic to 5G-Advanced Network Slicing. Vehicular Technology : IEEE 100th Conference VTC2024-Fall. Washington DC, 7–10 October, 2024 : proceedings, pp. 1–7. DOI: 10.1109/VTC2024-Fall63153.2024.10757954
Tselios C., Politis I., Amaxilatis D., Akrivopoulos O., Chatzigiannakis I., Panagiotakis S., Markakis E. K. Melding Fog Computing and IoT for Deploying Secure, Response-Capable Healthcare Services in 5G and Beyond. Sensors, 2022, Vol. 22, No. 9, Art. 3375, pp. 1– 14. DOI: 10.3390/s22093375.
Salhab N., Langar R., Rahim R. 5G network slices resource orchestration using Machine Learning techniques. Computer Networks, 2021, Vol. 188, pp. 1– 15. DOI: 10.1016/j.comnet.2021.107829.
Cai Y., Cheng P., Chen Z., Ding M., Vucetic B., Li Y. Deep Reinforcement Learning for Online Resource Allocation in Network Slicing. IEEE Transactions on Mobile Computing, 2024, Vol. 23, No. 6, pp. 7099–7116. DOI: 10.1109/TMC.2024.3344556.
Lin R., Liu H., Shan L., Zukerman M. Energy-aware Service Function Chaining Embedding in NFV Networks. IEEE Transactions on Services Computing, 2022, Vol. 99, pp. 1–14. DOI: 10.1109/TSC.2022.3162328
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 S. V. Sulima, Ie. D. Karashevych
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Creative Commons Licensing Notifications in the Copyright Notices
The journal allows the authors to hold the copyright without restrictions and to retain publishing rights without restrictions.
The journal allows readers to read, download, copy, distribute, print, search, or link to the full texts of its articles.
The journal allows to reuse and remixing of its content, in accordance with a Creative Commons license СС BY -SA.
Authors who publish with this journal agree to the following terms:
-
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License CC BY-SA that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
-
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
-
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) as it can lead to productive exchanges, as well as earlier and greater citation of published work.
