Network Slicing Automation: Challenges and Benefits

Network slicing is a technique widely used in 5G networks where multiple logical networks (i.e., slices) run over a single shared physical infrastructure. Each slice may realize one or multiple services, whose specific requirements are negotiated beforehand and regulated through Service Level Agreements (SLAs). In Beyond 5G (B5G) networks it is envisioned that slices should be created, deployed, and managed in an automated fashion (i.e., without human intervention) irrespective of the technological and administrative domains over which a slice may span. Achieving this vision requires a combination of novel physical layer technologies, artificial intelligence tools, standard interfaces, network function virtualization, and software-defined networking principles. This paper provides an overview of the challenges facing network slicing automation with a focus on transport networks. Results from a selected group of use cases show the benefits of applying conventional optimization tools and machine-learning-based techniques while addressing some slicing design and provisioning problems.

[1]  Ahmed Meddahi,et al.  NFV Security Survey: From Use Case Driven Threat Analysis to State-of-the-Art Countermeasures , 2018, IEEE Communications Surveys & Tutorials.

[2]  Nicolas K. Fontaine,et al.  Photonic lantern as mode multiplexer for multimode optical communications , 2017 .

[3]  Zbigniew Kotulski,et al.  On end-to-end approach for slice isolation in 5G networks. Fundamental challenges , 2017, 2017 Federated Conference on Computer Science and Information Systems (FedCSIS).

[4]  Danish Rafique,et al.  Machine learning for network automation: overview, architecture, and applications [Invited Tutorial] , 2018, IEEE/OSA Journal of Optical Communications and Networking.

[5]  Federico Tonini,et al.  Optimization of Optical Aggregation Network for 5G URLLC Service , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).

[6]  C. Raffaelli,et al.  Minimizing delay and packet delay variation in switched 5G transport networks , 2019, IEEE/OSA Journal of Optical Communications and Networking.

[7]  Lisandro Zambenedetti Granville,et al.  Network slicing security: Challenges and directions , 2019, Internet Technol. Lett..

[8]  Dengyin Zhang,et al.  Resource Allocation for Network Slicing in 5G Telecommunication Networks: A Survey of Principles and Models , 2019, IEEE Network.

[9]  Marco Ruffini,et al.  An Overview on Application of Machine Learning Techniques in Optical Networks , 2018, IEEE Communications Surveys & Tutorials.

[10]  Lena Wosinska,et al.  Dynamic slicing approach for multi-tenant 5G transport networks [invited] , 2018, IEEE/OSA Journal of Optical Communications and Networking.

[11]  Lena Wosinska,et al.  Machine Learning Aided Orchestration in Multi-tenant Networks , 2018, 2018 IEEE Photonics Society Summer Topical Meeting Series (SUM).

[12]  Yue Wang,et al.  Artificial Intelligence for Elastic Management and Orchestration of 5G Networks , 2019, IEEE Wireless Communications.

[13]  L. Wosinska,et al.  Cost Benefits of Centralizing Service Processing in 5G Network Infrastructures , 2019, 2019 Asia Communications and Photonics Conference (ACP).

[14]  Oriol Sallent,et al.  On 5G Radio Access Network Slicing: Radio Interface Protocol Features and Configuration , 2018, IEEE Communications Magazine.

[15]  Roberto Riggio,et al.  Intent-based mobile backhauling for 5G networks , 2016, 2016 12th International Conference on Network and Service Management (CNSM).

[16]  Marija Furdek,et al.  Machine Learning for Optical Network Security Management , 2020, 2020 Optical Fiber Communications Conference and Exhibition (OFC).

[17]  Gang Feng,et al.  Reconfiguration in Network Slicing—Optimizing the Profit and Performance , 2019, IEEE Transactions on Network and Service Management.

[18]  Tarik Taleb,et al.  On Multi-Domain Network Slicing Orchestration Architecture and Federated Resource Control , 2019, IEEE Network.

[19]  Yongli Zhao,et al.  Flexible RAN: Combining Dynamic Baseband Split Selection and Reconfigurable Optical Transport to Optimize RAN Performance , 2020, IEEE Network.

[20]  Hong,et al.  A Network Slicing for 5G Networks , 2017 .

[21]  Sakir Sezer,et al.  A Survey of Security in Software Defined Networks , 2016, IEEE Communications Surveys & Tutorials.

[22]  Roberto Proietti,et al.  Hierarchical Learning for Cognitive End-to-End Service Provisioning in Multi-Domain Autonomous Optical Networks , 2019, Journal of Lightwave Technology.

[23]  Raouf Boutaba,et al.  Reliable Slicing of 5G Transport Networks with Dedicated Protection , 2019, 2019 15th International Conference on Network and Service Management (CNSM).

[24]  Lazaros Gkatzikis,et al.  The Algorithmic Aspects of Network Slicing , 2017, IEEE Communications Magazine.

[25]  Tarik Taleb,et al.  Network Slicing and Softwarization: A Survey on Principles, Enabling Technologies, and Solutions , 2018, IEEE Communications Surveys & Tutorials.

[26]  Roger Piqueras Jover,et al.  5G NR Jamming, Spoofing, and Sniffing: Threat Assessment and Mitigation , 2018, 2018 IEEE International Conference on Communications Workshops (ICC Workshops).

[27]  Kimmo Kettunen,et al.  Transport evolution for the RAN of the future [Invited] , 2019, IEEE/OSA Journal of Optical Communications and Networking.

[28]  Lena Wosinska,et al.  Experimental Study of Machine-Learning-Based Detection and Identification of Physical-Layer Attacks in Optical Networks , 2019, Journal of Lightwave Technology.