In vehicular edge computing (VEC), multi-access edge computing (MEC) plays a crucial role in enabling vehicles to offload computationally intensive tasks, thereby enhancing data processing efficiency. However, MEC encounters challenges related to limited resources and the complexity of optimal task offloading due to the dynamicity of the environment. This short paper provides a preliminary analysis of decision-making algorithms responsible for assigning vehicle tasks to MECs, with a focus on the latency metric. The study examines two types of vehicle applications-safety and infotainment-using the open source Simu5G simulator to assess how environmental complexity and different policies influence the delay. The results underscore the impact of these factors on the end-to-end (E2E) latency and highlight potential enhancements. Future work will explore strategies to further reduce E2E latency, improving the overall responsiveness and efficiency of VEC systems.