Network slicing with load-balancing for task offloading in vehicular edge computing

Abstract

The support of edge computing for vehicular technologies gained increasing momentum with 5G to fulfill efficient offloading tasks from vehicles towards the edge nodes. Accordingly, vehicles demanding powerful computation and large storage resources will be directed to communicate with the nearest edge computing nodes hosted at a wireless 5G new generation nodes (gNBs) or a road side units (RSUs). To efficiently utilize the edge nodes' resources, network slicing and load-balancing features can greatly help in that, therefore, this paper proposes an algorithm for Vehicular Edge Computing (VEC) with network slicing and load-balancing based on resources utilization, denoted as VECSlic-LB, specifically dedicated for offloading tasks from vehicles to edge nodes at gNBs or RSUs. The algorithm can holistically view and manage the whole network, and use network function virtualization framework to manage the data plane. VECSlic-LB can handle a mix of slicing configurations, capable of balancing the loads between various slices per node, and can support multiple edge computing nodes. Several simulations were conducted comparing the performance of the proposed algorithm to the optimal solution, resulting on very close acceptance ratios as the optimal solution, and also was evaluated against a recent reference algorithm, providing more efficient resource utilizations ratios, saving up to 48% of the resources better than the state-of-art algorithm.