ANALISIS NETWORK SLICING DALAM MENINGKATKAN FLEKSIBILITAS LAYANAN PADA JARINGAN 5G: STUDI TINJAUAN LITERATUR SISTEMATIS
DOI:
https://doi.org/10.62671/suliwa.v3i2.263Keywords:
Network Slicing, 5G, SDN, NFV, MEC, Service FlexibilityAbstract
The development of 5G networks requires flexible mechanisms to support increasingly diverse and complex service demands. Network slicing enables the partitioning of a physical network into multiple virtual networks tailored to specific service requirements. This study aims to analyze the role of network slicing in enhancing service flexibility in 5G using a Systematic Literature Review (SLR) approach, focusing on publications from 2020 to 2025. The analysis examines key aspects, including network architecture, enabling technologies such as Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Multi-access Edge Computing (MEC), as well as their application in supporting various 5G service categories. The results indicate that network slicing enables dynamic and efficient resource allocation across heterogeneous services, improves service isolation, and enhances overall network management. It effectively supports major 5G service types, including enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and massive Machine Type Communications (mMTC). However, several challenges remain, such as end-to-end orchestration complexity, interoperability issues, and limited real-world implementation. This study provides a comprehensive overview of network slicing and identifies key research gaps and future research directions.
References
Barakabitze, A., Ahmad, A., Mijumbi, R., & Hines, A. (2020). 5G network slicing using SDN and NFV: A survey of taxonomy, architectures and future challenges. Computer Networks, 167, 106984. https://doi.org/10.1016/j.comnet.2019.106984
Cunha, J., Ferreira, P., Castro, E. M., Oliveira, P. C., Nicolau, M. J., Núñez, I., Sousa, X. R., & Serôdio, C. (2024). Enhancing network slicing security: Machine learning, software-defined networking, and network functions virtualization-driven strategies. Future Internet, 16(7), 226. https://doi.org/10.3390/fi16070226
Dubey, M., Singh, A. K., & Mishra, R. (2024). AI based resource management for 5G network slicing: History, use cases, and research directions. Concurrency and Computation: Practice and Experience, e8150. https://doi.org/10.1002/cpe.8327
Filali, A., Mlika, Z., Cherkaoui, S., & Kobbane, A. (2022). Dynamic SDN-based radio access network slicing with deep reinforcement learning for URLLC and eMBB services. IEEE Transactions on Network Science and Engineering, 9(2), 824–837. 10.48550/arXiv.2202.06435
Hurtado Sánchez, J. A., Casilimas, K., & Rendón, O. (2022). Deep reinforcement learning for resource management on network slicing: A survey. Sensors, 22(7), 2623. https://doi.org/10.3390/s22083031
Nadeem, L., Amin, Y., Loo, J., Azam, M. A., & Chai, K. K. (2022). Quality of service based resource allocation in D2D enabled 5G-CNs with network slicing. Physical Communication, 51, 101523. http://dx.doi.org/10.1016/j.phycom.2022.101703
Sanchez Iborra, R., Santa, J., Gallego Madrid, J., Covaci, S., & Gómez Skarmeta, A. F. (2019). Empowering the Internet of Vehicles with multi-RAT 5G network slicing. Sensors, 19(14), 3107. https://doi.org/10.3390/s19143107
Shen, X., Gao, J., Wu, W., Li, M., Zhou, C., & Zhuang, W. (2023). Holistic network virtualization and pervasive network intelligence for 6G. IEEE Communications Surveys & Tutorials, 25(1), 566–603. https://doi.org/10.48550/arXiv.2301.00519
Song, F., Li, J., Ma, C., Zhang, Y., Shi, L., & Jayakody, D. N. K. (2020). Dynamic virtual resource allocation for 5G and beyond network slicing. IEEE Open Journal of Vehicular Technology, 1, 45–66. https://ieeexplore.ieee.org/document/9078057
Wijethilaka, S., & Liyanage, M. (2021). Survey on network slicing for Internet of Things realization in 5G networks. IEEE Communications Surveys & Tutorials, 23(2), 957–994. https://ieeexplore.ieee.org/document/9382385
Wu, Y.-J., Hwang, W.-J., Shen, C.-Y., & Chen, Y.-Y. (2022). Network slicing for mMTC and URLLC using software-defined networking with P4 switches. Electronics, 11(13), 2058 https://www.mdpi.com/2079-9292/11/14/2111
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Dian Indah Lestari, Vera Veronica, Andi Ahmad Dahlan , Yulindon Khaidir (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
