Abstract
As telcos increasingly adopt cloud-native solutions, classic resource management problems within cloud environments have surfaced. While considerable attention has been directed toward the conventional challenges of dynamically scaling resources to adapt to variable workloads, the 5G promises of Ultra-Reliable Low Latency Communication (URLLC) remain far from being realized. To address this challenge, the current trend leans toward relocating network functions closer to the edge, following the paradigm of Mobile Edge Computing (MEC), or exploring hybrid approaches. The adoption of a hybrid cloud architecture emerges as a solution to alleviate the problem of the lack of resources at the edge by offloading network functions and workload from the Edge Cloud (EC) to the Central Cloud (CC) when edge resources reach their capacity limits. This paper focuses on the dynamic task offloading of network functions from ECs to CCs within cloud architectures in the ADAPTO framework.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Pham, Q.-V., et al.: A survey of multi-access edge computing in 5G and beyond: fundamentals, technology integration, and state-of-the-art. IEEE Access 8, 116974–117017 (2020)
3GPP: System architecture for the 5G System (5GS) (2022). v16.12.0
Harutyunyan, D., Behravesh, R., Slamnik-Kriještorac, N.: Cost-efficient placement and scaling of 5G core network and MEC-enabled application VNFs. In: 2021 IFIP/IEEE International Symposium on Integrated Network Management (IM), pp. 241–249. IEEE (2021)
Kekki, S., et al.: MEC in 5G networks. ETSI White Paper 28(2018), 1–28 (2018)
Zhang, Q., Liu, F., Zeng, C.: Adaptive interference-aware VNF placement for service-customized 5G network slices. In: IEEE INFOCOM 2019-IEEE Conference on Computer Communications, pp. 2449–2457. IEEE (2019)
Corici, M., Chakraborty, P., Magedanz, T.: A study of 5G edge-central core network split options. Network 1(3), 354–368 (2021)
Agbo-Adelowo, P., Weitkemper, P.: Analysis of different MEC offloading scenarios with LEO satellite in 5G networks. In: 2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS), pp. 1–6. IEEE (2023)
Ahamed, Md.M., Faruque, S.: 5G backhaul: requirements, challenges, and emerging technologies. Broadband Commun. Netw. Recent Adv. Lessons Pract. 43, 2018 (2018)
Hung, M.-H., Teng, C.-C., Chuang, C.-P., Hsu, C.-S., Gong, J.-W., Chen, M.-C.: A SDN controller monitoring architecture for 5G backhaul networks. In: 2022 23rd Asia-Pacific Network Operations and Management Symposium (APNOMS), pp. 1–4. IEEE (2022)
Leivadeas, A., Pitaev, N., Falkner, M.: Analyzing the performance of SD-WAN enabled service function chains across the globe with AWS. In: Proceedings of the 2023 ACM/SPEC International Conference on Performance Engineering, pp. 125–135 (2023)
Corici, M., et al.: SATis5 solution: a comprehensive practical validation of the satellite use cases in 5G. In: Proceedings of the 24th Ka and Broadband Communications Conference, Niagara Falls, ON, Canada, pp. 15–18 (2018)
Zhang, Y., Xu, C., Muntean, G.-M.: Revenue-oriented service offloading through fog-cloud collaboration in SD-WAN. In: GLOBECOM 2022-2022 IEEE Global Communications Conference, pp. 5753–5758. IEEE (2022)
Acknowledgment
This work was partially supported by the European Union through the ADAPTO project, part of the RESTART program, NextGenerationEU PNRR, CUP E63C2 2002040007, CP PE0000001.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Botta, A. et al. (2024). Edge to Cloud Network Function Offloading in the ADAPTO Framework. In: Barolli, L. (eds) Advanced Information Networking and Applications. AINA 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 203. Springer, Cham. https://doi.org/10.1007/978-3-031-57931-8_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-57931-8_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-57930-1
Online ISBN: 978-3-031-57931-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)