# Minimum-Cost Offloading for Collaborative Task Execution of MEC-Assisted Platooning
## 1 Introduction
First, the video sources must be collected by the local device. Second, the tracker, mapper and object recognizer are the computation-intensive components, which can be offloaded to the MEC server to reduce the computing time of the tasks and meet the task execution time requirements. Third, render must be executed locally and the calculated results need to be sent back to the local device for display. There are many relative applications for V2X services like Forward Collision Warning, Pre-crash Sensing Warning, V2X Road Safety Service via Infrastructure [10]. Due to the limited size of the resource space, the available resources of the vehicle terminals cannot meet the service requirements, which will cause high application processing delay, and even lead to safety accidents and so on. In addition, the current centralized server deployment in cellular network is not sufficient to meet future service requirements, such as delay, reliability and availability, which are key parameters. End-to-end delay must be reduced to within the range of service requirements (non-security services: 100 ms to 1 s, security related services or delay-sensitive services: less than 20 ms [7]). For instance, in current network architecture, offloading the workload of the vehicle terminal to the cloud center will cause extremely high transmission delay and it is difficult to achieve real-time data transmission [11]. The tension between computing resource-hungry applications and vehicle terminals with limited computing resources poses a major challenge to the development of  
Figure 1: Platooning composition. mobile platforms [12]. In this paper, we consider a platoon with an MEC server to assist offload the workload of the vehicle terminal. Specifically, within the deadline of the application tasks, the resource purchase cost is employed to determine whether the tasks are executed locally, or offloaded to the other platoon members, or to the MEC server. We aim to find an optimal collaborative offloading decision among the members of the platoon and the MEC, and to execute the tasks with minimum resource purchase cost by the vehicle user within the deadline of the tasks. Mathematically, we model a minimum-cost task offloading problem as a constrained shortest path problem on a directed acyclic graph. Then we use the classical Lagrangian Relaxation-based Aggregated Cost (LARAC) algorithm to obtain an optimal solution of the constrained optimization problem. Finally, the simulation results of inputting the existing measurement data show that the offloading strategy is the optimal offloading strategy. Comparing the task execution locally or offloaded to the other members of the platoon, or offloaded the task to the MEC server, the cooperative task execution can complete the task within the task deadline at the minimum cost. The rest of the paper is organized as follows. Section 2 reviews the relevant recent works. We give the system model in Section 3. In Section 4, delay constrained offloading decision is modeled as a constrained shortest path problem. Then we get an optimal strategy of offloading decision for collaborative task execution in Section 5. Section 6 shows the numerical analysis of offloading decision procedure, and Section 7 concludes the paper.