Reliability-Aware Task Replication for Mobile Edge Computing

Lipei Yang, Ao Zhou, Xiao Ma, Yiran Zhang, Shangguang Wang

Abstract: As infrastructure deployment continues to expand worldwide, the development of the Internet of Vehicles has become increasingly feasible. With widespread cellular connectivity and powerful roadside computing capabilities, advanced driving assistance systems can now rely on roadside decision models in addition to vehicle-side ones, overcoming the limitations of a single vehicle’s perception range. This shift has led to improvements in manufacturing efficiency, cruising range, and battery life of intelligent vehicles. However, maintaining the ultralow latency and high-reliability requirements of on-vehicle services is still a challenge due to air interface fluctuations and edge server computing loads, which could potentially jeopardize driving safety. To tackle this issue, we conducted real-world measurements of edge server access delay in LTE and 5G cellular networks. Our analysis identified key factors affecting delay distribution, leading to the development of an approximate fitting function for the delay probability density function. We also proposed a reliability-aware task replication algorithm that leverages delay samples and edge server status information to make real-time task replication and offloading decisions, minimizing replication while ensuring service reliability. Simulations based on real-world data sets indicate our approach reduces task completion delay by up to 42.11% and limits the maximum task replication redundancy peak value to 63.37%, effectively ensuring the reliability of on-vehicle services during driving.