Joint Network Slicing, Routing, and In-Network Computing for Energy-Efficient 6G (2401.06306v1)
Abstract: To address the evolving landscape of next-generation mobile networks, characterized by an increasing number of connected users, surging traffic demands, and the continuous emergence of new services, a novel communication paradigm is essential. One promising candidate is the integration of network slicing and in-network computing, offering resource isolation, deterministic networking, enhanced resource efficiency, network expansion, and energy conservation. Although prior research has explored resource allocation within network slicing, routing, and in-network computing independently, a comprehensive investigation into their joint approach has been lacking. This paper tackles the joint problem of network slicing, path selection, and the allocation of in-network and cloud computing resources, aiming to maximize the number of accepted users while minimizing energy consumption. First, we introduce a Mixed-Integer Linear Programming (MILP) formulation of the problem and analyze its complexity, proving that the problem is NP-hard. Next, a Water Filling-based Joint Slicing, Routing, and In-Network Computing (WF-JSRIN) heuristic algorithm is proposed to solve it. Finally, a comparative analysis was conducted among WF-JSRIN, a random allocation technique, and two optimal approaches, namely Opt-IN (utilizing in-network computation) and Opt-C (solely relying on cloud node resources). The results emphasize WF-JSRIN's efficiency in delivering highly efficient near-optimal solutions with significantly reduced execution times, solidifying its suitability for practical real-world applications.
- “IMT Traffic Estimates for the Years 2020 to 2030,” International Telecommunication Union (ITU), Tech. Rep. ITU-R M.2370-0, Jul. 2015. [Online]. Available: https://www.itu.int/pub/R-REP-M.2370-2015
- H. Yu, M. Shokrnezhad et al., “Toward 6G-Based Metaverse: Supporting Highly-Dynamic Deterministic Multi-User Extended Reality Services,” IEEE Network, vol. 37, no. 4, pp. 30–38, Jul. 2023.
- S. Kianpisheh and T. Taleb, “A Survey on In-Network Computing: Programmable Data Plane and Technology Specific Applications,” IEEE Communications Surveys & Tutorials, vol. 25, no. 1, pp. 701–761, 2023.
- L. U. Khan, I. Yaqoob et al., “Network Slicing: Recent Advances, Taxonomy, Requirements, and Open Research Challenges,” IEEE Access, vol. 8, pp. 36 009–36 028, 2020.
- I. Afolabi, T. Taleb et al., “Network Slicing and Softwarization: A Survey on Principles, Enabling Technologies, and Solutions,” IEEE Communications Surveys & Tutorials, vol. 20, no. 3, pp. 2429–2453, 2018.
- A. Sapio, I. Abdelaziz et al., “In-Network Computation is a Dumb Idea Whose Time Has Come,” in Proceedings of the 16th ACM Workshop on Hot Topics in Networks, ser. HotNets-XVI. New York, NY, USA: Association for Computing Machinery, Nov. 2017, pp. 150–156.
- T. A. Benson, “In-Network Compute: Considered Armed and Dangerous,” in Proceedings of the Workshop on Hot Topics in Operating Systems, ser. HotOS ’19. New York, NY, USA: Association for Computing Machinery, May 2019, pp. 216–224.
- Z. Sasan and S. Khorsandi, “Slice-Aware Resource Calendaring in Cloud-based Radio Access Networks,” in 2022 30th International Conference on Electrical Engineering (ICEE), May 2022, pp. 1005–1009.
- N. Hu, Z. Tian, X. Du, and M. Guizani, “An Energy-Efficient In-Network Computing Paradigm for 6G,” IEEE Transactions on Green Communications and Networking, vol. 5, no. 4, pp. 1722–1733, Dec. 2021.
- M. Shokrnezhad and T. Taleb, “Near-optimal Cloud-Network Integrated Resource Allocation for Latency-Sensitive B5G,” in GLOBECOM 2022 - 2022 IEEE Global Communications Conference, Dec. 2022, pp. 4498–4503.
- M. Shokrnezhad, T. Taleb et al., “Double Deep Q-Learning-based Path Selection and Service Placement for Latency-Sensitive Beyond 5G Applications,” IEEE Transactions on Mobile Computing, pp. 1–14, 2023.
- W.-K. Chen, Y.-F. Liu et al., “Network Slicing for Service-Oriented Networks with Flexible Routing and Guaranteed E2E Latency,” in 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), May 2020, pp. 1–5.
- T. Dong, Z. Zhuang et al., “Intelligent Joint Network Slicing and Routing via GCN-Powered Multi-Task Deep Reinforcement Learning,” IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 2, pp. 1269–1286, Jun. 2022.
- “Challenges on Global Electricity Usage of Communication Technology: Trends to 2030.” [Online]. Available: https://www.mdpi.com/2078-1547/6/1/117
- S. Maher, M. Miltenberger et al., “PySCIPOpt: Mathematical Programming in Python with the SCIP Optimization Suite,” in Mathematical Software – ICMS 2016, ser. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2016, pp. 301–307.
- M. Farhoudi, M. Shokrnezhad et al., “Qos-aware service prediction and orchestration in cloud-network integrated beyond 5g,” arXiv preprint arXiv:2309.10185, 2023.
- M. Shokrnezhad, S. Khorsandi et al., “A Scalable Communication Model to Realize Integrated Access and Backhaul (IAB) in 5G,” in ICC 2023 - IEEE International Conference on Communications, May 2023, pp. 1350–1356.
- J. Prados-Garzon, T. Taleb et al., “Deterministic 6GB-Assisted Quantum Networks with Slicing Support: A New 6GB Use Case,” IEEE Network, pp. 1–1, 2023.