Task Offloading and Scheduling Under Hard Deadlines in Vehicular Edge Computing Systems

In this correspondence, we consider vehicular edge computing systems in which computing tasks arrive randomly to vehicles over time and are offloaded to one of the edge servers. Unlike previous work that assumes ideal and global server information, we study a practical online framework for acquiring edge server information. We propose an online offloading and scheduling algorithm that efficiently integrates the Moore–Hodgson algorithm into this practical framework to satisfy hard deadlines required by tasks. Numerical experiments demonstrate that the proposed algorithm outperforms conventional first-come-first-serve scheduling and D-Dedas scheduling. Furthermore, the results show that the performance of proactive broadcasting of server information closely approaches that of ideal server information as the number of vehicles increases.