4.7 Article

End-to-End Slicing With Optimized Communication and Computing Resource Allocation in Multi-Tenant 5G Systems

Journal

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume 69, Issue 2, Pages 2079-2091

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2019.2959193

Keywords

Radio Access Network (RAN); Multi-access Edge Computing (MEC); slicing; computing resource; communication resource; virtualization; optimization

Funding

  1. H2020 Collaborative Europe/Taiwan Research Project5G-CORAL [761586]
  2. Duan Jin Research Project in the Institute of Information and Communications from the Industrial Technology Research Institute, Taiwan

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Slicing is a key technology in 5G networks to provide scalability and flexibility in allocating computing and communication resources among multiple tenants. Typically, 5G networks have a 2-tier architecture consisting of a central office and transport network in the upper tier and a multi-access edge and radio access network in the lower tier. The tenants which share the 2-tier architecture typically have different service-dependent resource requirements. This study proposes an algorithm, designated as Upper-tier First with Latency-bounded Over-provisioning Prevention (UFLOP), to adjust the capacity and traffic allocation in such a way as to minimize the over-provisioning ratio while still satisfying the latency constraints and Service Level Agreements (SLAs) of the tenants. The performance of UFLOP is evaluated experimentally with a real testbed on an end-to-end slicing framework using three typical 5G services, namely Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency (URLLC), and massive Machine Type Connection (mMTC). It is shown that UFLOP successfully determines the critical traffic allocation ratio between the central office and the edge which achieves an over-provisioning ratio close to zero while still meeting the latency requirements. The results suggest optimal resource allocation ratios of 10:0, 1.5:8.5 and 7.8:2.2 for the eMBB, URLLC and mMTC applications, respectively. Furthermore, it is shown that the computing resource behaves as a bottleneck for the eMBB and mMTC services, while the communication resource serves as a bottleneck for the URLLC service.

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