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

2025-03-25

AI Summary

To address the challenge of simultaneously ensuring real-time performance and reliability under highly dynamic task streams in 5G vehicular edge computing (VEC), this paper proposes an end-edge collaborative task offloading and hybrid scheduling framework. We innovatively design a dual-partition mechanism—comprising Mobile Edge Computing (MEC) and local execution—that guarantees zero task dropping. Furthermore, we formulate a multi-objective scheduling model integrating Particle Swarm Optimization (PSO) to jointly optimize offloading decisions, computational resource allocation, and dynamic bandwidth configuration. Experimental results demonstrate that our approach achieves a 100% task completion rate. Compared with a pure MEC-based scheme, it incurs only marginal end-to-end latency increase while completely eliminating task loss. Moreover, the shared-bandwidth strategy significantly reduces transmission queuing delay. This work establishes a practical, deployable VEC resource coordination paradigm tailored for ultra-low-latency, high-reliability applications such as autonomous driving.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Learning on the Edge & Model Compression
• Game Theory and Economic Paradigms: Coordination and Collaboration

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Optimizing task offloading in 5G vehicular edge computing
• Reducing latency for real-time vehicular applications
• Balancing task partitioning between MEC servers and vehicles

Innovation

• Task scheduling with FCFS, SDF, PSO
• Partitioning tasks between MEC and vehicles
• Shared bandwidth for transmission efficiency