Distributed Network Slicing for Time-Sensitive Edge Learning in Edge Computing-Supported IoT Networks

Network slicing is one of the key enablers for B5G and 6G to support diversified IoT services and application scenarios. In this paper, the problem of network slicing for supporting time-sensitive edge learning in massive-scale IoT networks is studied. In particular, a novel distributed network slicing framework based on a new control plane entity, called D-orchestrator, is proposed. This framework can jointly optimize the allocation and orchestration of communication and edge computational resources without requiring exchanges of the local data or resource information between base stations (BSs) and edge servers. A distributed joint resource allocation algorithm is developed based on the alternating direction method of multipliers with partial variable splitting (DistADMM-PVS) that minimizes the average service response-time of a set of service instances when the coordination among the D-orchestrator, BSs, and edge servers is perfectly synchronized. Motivated by the observation that the synchronization of coordination may result in high coordination delay that can be intolerable in many practical scenarios, particularly for large IoT networks, a novel asynchronized ADMM (AsyncADMM) algorithm is proposed. In AsyncADMM, the D-orchestrator, BSs, and edge servers can be coordinated asynchronously. AsyncADMM is then shown to converge to the global optimal solution with improved scalability and negligible coordination delay. The performance of the proposed framework is evaluated using two-month of traffic data collected in an in-campus smart transportation system supported by a 5G network. Extensive simulations are conducted for both pedestrian and vehicular-related services during peak and non-peak hours. Simulation results show that the proposed distributed network slicing framework offers a significant reduction in the service response time for both supported services.