A Reliable and Efficient 5G Vehicular MEC: Guaranteed Task Completion with Minimal Latency

Abstract

This paper explores the advancement of Vehicular Edge Computing (VEC) as a tailored application of Mobile Edge Computing (MEC) for the automotive industry, addressing the rising demand for real-time processing in connected and autonomous vehicles. VEC brings computational resources closer to vehicles, reducing data processing delays crucial for safety-critical applications such as autonomous driving and intelligent traffic management. However, the challenge lies in managing the high and dynamic task load generated by vehicles' data streams. We focus on enhancing task offloading and scheduling techniques to optimize both communication and computation latencies in VEC networks. Our approach involves implementing task scheduling algorithms, including First-Come, First-Served (FCFS), Shortest Deadline First (SDF), and Particle Swarm Optimization (PSO) for optimization. Additionally, we divide portions of tasks between the MEC servers and vehicles to reduce the number of dropped tasks and improve real-time adaptability. This paper also compares fixed and shared bandwidth scenarios to manage transmission efficiency under varying loads. Our findings indicate that MEC+Local (partitioning) scenario significantly outperforms MEC-only scenario by ensuring the completion of all tasks, resulting in a zero task drop ratio. The MEC-only scenario demonstrates approximately 5.65% better average end-to-end latency compared to the MEC+Local (partitioning) scenario when handling 200 tasks. However, this improvement comes at the cost of dropping a significant number of tasks (109 out of 200). Additionally, allocating shared bandwidth helps to slightly decrease transmission waiting time compared to using fixed bandwidth.