Adaptive Computation Offloading with Task Scheduling Minimizing Reallocation in VANETs

Abstract

Computation Offloading (CO) can accelerate application running through parallel processing. This paper proposes a method called vehicular adaptive offloading (VAO) in which vehicles in vehicular ad-hoc networks (VANETs) offload computationally intensive tasks to nearby vehicles by taking into account the dynamic topology change of VANETs. In CO, task scheduling has a huge impact on overall performance. After representing the precedence relationship between tasks with a directed acyclic graph (DAG), VAO in the CO requesting vehicle assigns tasks to neighbors so that it can minimize the probability of task reallocations caused by connection disruption between vehicles in VANETs. The simulation results showed that the proposed method decreases the number of reallocations by 45.4%, as compared with the well-known task scheduling algorithms HEFT and Max-Min. Accordingly, the schedule length of entire tasks belonging to one application is shortened by 14.4% on average.