SFO: An Adaptive Task Scheduling based on Incentive Fleet Formation and Metrizable Resource Orchestration for Autonomous Vehicle Platooning

Autonomous vehicle platooning has tremendous potential to relieve the burden of Vehicular Edge Computing (VEC) by sharing resources with nearby vehicles. Therefore, fleet formation and resource orchestration within vehicle platoons have recently ignited significant research interest. However, most fleet formation works focus on the intra-platoon configuration and information exchange, but few consider trajectory matching and joining willingness. Likewise, in multi-platoon scenarios, static resource orchestration for a single platoon no longer meets the demand from dynamic resource scheduling. To tackle these problems, we proposed the SFO scheme, an adaptive task S cheduling based on incentive fleet F ormation and metrizable resource O rchestration. First, we design a fleet F ormation algorithm based on T rajectory matching and J oining willingness (FTJ) to ensure the stable underlying architecture. Second, we use the W eighted S um of E nergy C onsumption (WSEC) as the performance metric for resource orchestration and formulate the time-average WSEC minimization problem. Third, an A daptive task S cheduling under P artitionable A pplications and variable R esources (ASPAR) is proposed for an asymptotic optimal solution in reaction to the changeable backlog of the timeout queue. Finally, our numerical results demonstrate that our approach is superior to other latest and classic works in energy consumption and execution latency.