End-to-end network slicing in vehicular clouds using the MobFogSim simulator

Abstract

Fog computing and network slicing are drawing attention as promising technologies in modern networks, such as Vehicular Ad-Hoc Networks (VANETs). On the one hand, fog computing lets end-user devices offload part of their computation and data to micro data centres (i.e., fog nodes) that are pervasively deployed in their proximity. On the other hand, network slicing creates different logical networks over a common physical infrastructure, wherein each logical network, called a slice, aims at meeting the requirements and characteristics of a specific class of applications. In this context, simulators allow the evaluation of new solutions in a cost-efficient and repeatable way. MobFogSim is specifically targeted at allowing the evaluation of resource management solutions in modern networks, including the support of device mobility, fog service migration, and network slicing. In this work, we further extend MobFogSim by presenting new functionalities, namely: (i) the support of VANETs; (ii) the modelling of end-to-end (E2E) slices, which include storage and processing resources of fog nodes besides the network resources; and (iii) improvements to the scalability of our simulator. We validate this work by conducting experiments over MobFogSim to assess its improved scalability as well as to test network slicing and fog computing solutions in three different VANET scenarios: (i) fog_only, wherein fog nodes are deployed only at the edge of the fixed infrastructure; (ii) vehicular_only, where fog nodes are provided only by nearby vehicles; and (iii) hybrid, which combines the previous two. Results show that the vehicular_only approach can reduce the latency provided to end-user devices by around 50% with respect to the fog_only approach. Besides, the improved version of MobFogSim reduces the simulation time by around 65% if compared to the previous version.