Community Detection-Empowered Self-adaptive Network Slicing in Multi-Tier Edge-Cloud System

Network slicing (NS) is a highly promising paradigm in 5G and forthcoming 6G communication networks. NS allows for the customization of multiple logically independent network slices to provide tailored service for vertical applications with diverse quality of service (QoS) requirements. However, current research on NS primarily relies on the traditional modeling methods such as service function chaining (SFC) and task offloading, which have limitations in adapting to the evolving scenarios in 5G/6G networks. To address this, our study introduces one novel Self-adaptive Network Slicing (SNS) modeling method. In this approach, each service is abstracted as multiple SFC replicas originating from diverse access points. Based on the SNS modeling, we investigate a VNF configuration and flow routing (VCFR) problem for service provisioning in a multi-tier system. With the objective of achieving load-balancing with minimal slice operational expenditure, we formulate the VCFR as a mixed-integer linear programming. However, deriving an exact solution via MILP is computationally expensive due to its NP-hardness. To reduce computational complexity, we propose one Load Balancing-considered Community Detection-based Heuristic (LBCD-Heu), our divide and conquer approach, to solve the problem. In LBCD-Heu, we first design a load balancing-considered community detection method to divide the substrate multi-tier network into multiple independent communities. Following this, the MILP is employed in each community to obtain a near-optimal solution. Extensive evaluations justify that LBCD-Heu can effectively reduce the service operational cost and algorithm run-time while ensuring the load balancing of substrate network. Additionally, our results verify that the SNS modeling enables the provision of services at lower expenditures compared with traditional modeling methods.