Energy-Efficient Edge Computing Service Provisioning for Vehicular Networks: A Consensus ADMM Approach

In vehicular networks, in-vehicle user equipment (UE) with limited battery capacity can achieve opportunistic energy saving by offloading energy-hungry workloads to vehicular edge computing nodes via vehicle-to-infrastructure links. However, how to determine the optimal portion of workload to be offloaded based on the dynamic states of energy consumption and latency in local computing, data transmission, workload execution and handover, is still an open issue. In this paper, we study the energy-efficient workload offloading problem and propose a low-complexity distributed solution based on consensus alternating direction method of multipliers. By incorporating a set of local variables for each UE, the original problem, in which the optimization variables of UEs are coupled together, is transformed into an equivalent general consensus problem with separable objectives and constraints. The consensus problem can be further decomposed into a bunch of subproblems, which are distributed across UEs and solved in parallel simultaneously. Finally, the proposed solution is validated based on a realistic road topology of Beijing, China. Simulation results have demonstrated that significant energy saving gain can be achieved by the proposed algorithm.