CODE-V: Multi-hop computation offloading in Vehicular Fog Computing

Abstract

Vehicular Fog Computing (VFC) is an extension of fog computing in Intelligent Transportation Systems (ITS). It is an emerging computing model that leverages latency-aware and energy-aware application deployment in ITS. In this paper, we consider the problem of multi-hop computation offloading in a VFC network, where the client vehicles are connected to fog computing nodes by multi-hop LTE access points. Our scheme addresses three key aspects in a VFC architecture namely: (i) Optimal decision on local or remote task execution, (ii) Optimal fog node assignment, and (iii) Optimal path (multi-hop) selection for computation offloading. Considering the constraints on service latency, hop-limit, and computing capacity, the process of workload allocation across host vehicles, stationary and mobile fog nodes, and the cloud servers is formulated into a multi-objective, non-convex, and NP-hard Quadratic Integer Problem (QIP). Accordingly, an algorithm named Computation Offloading with Differential Evolution in VFC (CODE-V) is proposed. For each client task, CODE-V takes into account inter-fog cooperation, fog node acceptance probability, and the topological variations in the transportation fleets, towards optimal selection of a target fog node. We conduct extensive simulations on the real-world mobility traces of Shenzhen, China, to show that CODE-V reduces the average service latency and energy consumption by approximately 28% and 61%, respectively, compared to the state-of-the-art. Moreover, the CODE-V also gives better solution quality compared to standard DE∕rand∕1∕bin algorithm and the solutions generated by a CPLEX solver.