Network Slice Mobility and Service Function Chain Migration across Multiple Administrative Cloud Domains

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School of Electrical Engineering | Doctoral thesis (article-based) | Defence date: 2024-01-26
Degree programme
150 + app. 90
Aalto University publication series DOCTORAL THESES, 13/2024
The maturing 5G network technology sees growing commercial deployments, with a shifting focus to service delivery. 5G networks, a common platform for diverse services, utilize network slicing for service isolation. Cloud-native services, composed of interdependent micro-services, are allocated to network slices spanning multiple areas, domains, and data centers. Due to mobility events caused by mobile end-users, slices with their assigned resources and services need to be re-scoped and re-provisioned. This requires slice mobility, which involves a slice moving between service areas. Slice mobility requires the inter-dependent service and resources to be migrated to reduce system overhead and to ensure low-communication latency by following end-user mobility patterns. Recent advances in computational hardware, Artificial Intelligence, and Machine Learning have attracted interest within the communication community, with increased research interest in self-managed network slices. However, migrating a service instance of a slice remains an open and challenging process given the needed coordination between inter-cloud resources, the dynamics, and the constraints of inter-data center networks. In this regard, this dissertation defines and enables smooth network slicing mobility patterns while maintaining both system and network resources stable. Specifically, we design, implement, and evaluate our proposed migration framework. Then, we design and define different network slice mobility patterns with their corresponding grouping methods and relevant mobility triggers. Next, we introduce various SFC migration strategies as an underlay technology enabler for network slice mobility patterns. After that, we propose an agent for automating the triggers selection process for enabling various network slice mobility patterns. Finally, we develop a network-aware agent capable of selecting accurate bandwidth values while ensuring fast and reliable service migration, thus enabling slice mobility while matching network and system requirements. In each section of this dissertation, the research results are evaluated and validated under different configurations in real-world settings or simulated environments. This dissertation provides recommendations for improving and extending the notion of mobility in network slices while also highlighting the various outstanding questions and suggesting future challenges and research directions.
Supervising professor
Manner, Jukka, Prof., Aalto University, Department of Information and Communications Engineering, Finland
Thesis advisor
Dutra, Diego Leonel Cadette, Prof., Federal University of Rio de Janeiro, Brazil
5G, network slicing, software defined networking, network function virtualization, service function chain, multi-access edge computing, network softwarisation, deep reinforcement learning
Other note
  • [Publication 1]: R. A. Addad, D.L.C. Dutra, M. Bagaa, T. Taleb, and H. Flinck, “Towards A Fast Service Migration in 5G,” In 2018 IEEE Conference on Standards for Communications and Networking (CSCN), Paris, France, Oct 2018.
    DOI: 10.1109/CSCN.2018.8581836 View at publisher
  • [Publication 2]: R. A. Addad, D.L.C. Dutra, M. Bagaa, T. Taleb, and H. Flinck, “Fast Service Migration in 5G Trends and Scenarios,” IEEE Network Magazine, vol. 34, no. 2, pp. 92-98, Mar 2020.
    DOI: 10.1109/MNET.001.1800289 View at publisher
  • [Publication 3]: R. A. Addad, D.L.C. Dutra, M. Bagaa, T. Taleb, H. Flinck, and M. Namane, “Benchmarking the ONOS Intent interfaces to ease 5G service management,” In 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, UAE, Dec 2018.
    DOI: 10.1109/GLOCOM.2018.8648078 View at publisher
  • [Publication 4]: R. A. Addad, D.L.C. Dutra, M. Bagaa, T. Taleb, and H. Flinck, “MIRA!: A SDN-based Framework for Cross-Domain Fast Migration of Ultra-Low Latency 5G Services,” In 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, UAE, Dec 2018.
    DOI: 10.1109/GLOCOM.2018.8648002 View at publisher
  • [Publication 5]: R. A. Addad, T. Taleb, H. Flinck, M. Bagaa and D.L.C. Dutra, “Network Slice Mobility in Next Generation Mobile Systems: Challenges and Potential Solutions,” IEEE Network Magazine, vol. 34, no. 1, pp. 84-93, Jan 2020.
    DOI: 10.1109/MNET.2019.1800268 View at publisher
  • [Publication 6]: R. A. Addad, D.L.C. Dutra, M. Bagaa, T. Taleb, and H. Flinck, “Towards studying Service Function Chain Migration Patterns in 5G Networks and beyond,” In 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, Dec 2019.
    DOI: 10.1109/GLOBECOM38437.2019.9013983 View at publisher
  • [Publication 7]: R. A. Addad, D.L.C. Dutra, T. Taleb and H. Flinck, “Toward Using Reinforcement Learning for Trigger Selection in Network Slice Mobility,” IEEE Journal on Selected Areas in Communications (JSAC), vol. 39, no. 7, pp. 2241-2253, July 2021.
    DOI: 10.1109/JSAC.2021.3078501 View at publisher
  • [Publication 8]: R. A. Addad, D. L. C. Dutra, T. Taleb and H. Flinck, “AI-Based Network-Aware Service Function Chain Migration in 5G and Beyond Networks,” IEEE Transactions on Network and Service Management (TNSM), vol. 19, no. 1, pp. 472-484, Mar 2022.
    DOI: 10.1109/TNSM.2021.3074618 View at publisher