Towards Optimal Placement and Runtime Migration of Time-Sensitive Services of Connected and Automated Vehicles

Abstract

In this paper, the goal is to reduce the time needed for the placement and migration of services of Connected Automated Vehicles (CAVs) using precise hybrid positioning method. First, to place a service in a Multi-access Edge Computing (MEC) node, there should be sufficient resources in the served MEC node; otherwise, the service would be placed on the neighboring MEC node or even on the core node, resulting in higher delays. We start by modeling our problem with the aid of traffic theory to analytically obtain the necessary number of resources for achieving the desired delay. Second, to reduce the migration process delay, the migration should begin before the vehicle reaches the MEC node. Thus, an AI lane-based scheme is proposed to predict candidate nodes for migration based on precise positioning. Precise positioning data is acquired from a Real-Time Kinematic Global Navigation Satellite System (RTK- GNSS) measurement campaign. The obtained imbalanced raw data is treated and used in the prediction scheme, and the resulting prediction accuracy achieves 99.3%. Finally, we formulate a service placement and migration delay optimization problem and propose an algorithm to solve it. The algorithm shows a latency reduction of approximately 50% compared to the core placement and up to 29% compared to the benchmark prediction algorithm. Moreover, the simulation results for the proposed service placement and migration algorithm show that in case the MEC resource calculations are not used, the delay is 2.2 times greater than when they are used.

Keywords

• 5G
• beyond 5G
• MEC
• position prediction
• RTK
• service migration