Energy-Efficient Workload orchestration for 6G Vehicular Edge Computing

Vehicular edge computing for 6G must meet millisecond scale latency and stringent energy budgets over dynamic, multi tier networks. We introduce an ML driven orchestrator that uses a Naive Bayes classifier for edge and cloud tier selection with a regression model for service time prediction on multidimensional features (task attributes, network metrics, energy profiles, CPU load), and embeds a multipath feasibility module, augmented by transmission power control and dynamic CPU frequency scaling, to jointly optimize latency, reliability, and energy consumption. In comprehensive EdgeCloudSim SUMO experiments, our framework achieves a latency of up to 35% lower end- to-end, 30% fewer task failures, and keeps energy use within 10% of optimal compared to a randomized baseline. These results demonstrate millisecond scale decision capability and robust performance under realistic VEC conditions