Low-Latency Scheduling Approach for Dependent Tasks in MEC-Enabled 5G Vehicular Networks

With the development of the Internet of Vehicles (IoV), multiaccess edge computing (MEC) technology places computing resources closer to users at edge nodes, enabling faster, more reliable, and secure computing services. In the MEC-enabled IoV networks, task offloading scheduling, as an effective method to alleviate the computational burden on vehicles, is gaining increasing attention. However, with the intelligent and networked development of vehicles, the complex data dependency between in-vehicle tasks brings challenges to offloading scheduling. In contrast to many existing methods that solely address individual tasks, there is a growing need to tackle interrelated tasks within the IoV framework. This includes tasks like processing vehicle sensor data, gathering and analyzing road condition information, facilitating collaborative decision making among vehicles, and optimizing traffic signal systems. Our objective is to address the broader challenge of offloading dependent tasks, as this closely aligns with real-world scenes and requirements. In this article, we propose a priority-based task scheduling algorithm (PBTSA) to minimize processing delay when the tasks are interdependent. PBTSA proposes a method that can better measure the data transmission and calculation delay of the IoV networks. We first model dependent tasks as a directed acyclic graph (DAG) and then use the reverse breadth-first search (RBFS) algorithm to generate the priority of each subtask, and finally according to the priority with low complexity to offload subtasks greedily to minimize task processing delay. We compare the PBTSA with the other two existing algorithms through simulations. The results show that the PBTSA can effectively reduce the task processing delay and can reach close to 10%.