Purpose This study aims to investigate the impact of complex task dependencies and dynamic vehicular environments on task offloading efficiency in vehicular edge computing (VEC) systems, addressing scalable Quality of Service–aware scheduling for dependency-intensive Web services. It seeks new insights into minimizing latency and energy consumption while meeting service-level agreement (SLA) requirements, advancing understanding of resource management in intelligent vehicular networks. Design/methodology/approach This study proposes a dependency-aware Web-related task offloading framework that models interrelated tasks as directed acyclic graphs (DAGs). Data from simulated VEC scenarios – incorporating real-time vehicle mobility patterns, SLA-defined task priorities and road traffic density predictions – were analyzed through Python simulations to evaluate the proposed dynamic association particle swarm optimization (DAPSO) algorithm against benchmark schedule algorithms. Findings The results demonstrate that the DAPSO framework effectively reduces task offloading latency and energy consumption through predictive edge server resource reservation. This empirically validates the critical necessity of integrating dependency-aware heuristic algorithms with proactive resource allocation mechanisms to address NP-hard scheduling challenges in dynamic VEC environments. Originality/value By integrating DAG-based dependency modeling, real-time mobility awareness, SLA prioritization and predictive resource reservation into a unified VEC framework, this research provides theoretical and practical foundations for next-generation intelligent transportation systems. The DAPSO algorithm offers implementable solutions for latency-sensitive Internet of Vehicle applications while highlighting pathways for adaptive large-scale optimization.
Lin et al. (Thu,) studied this question.