Distributed Task Offloading and Resource Allocation in Vehicular Edge Computing

With the powerful storage and computation capability, vehicular edge computing (VEC) is considered as a promising paradigm to enhance the safety and service quality of vehicles in intelligent transportation systems (ITS). In this paper, we formulate a joint road side units (RSUs) selection and resource allocation problem, which minimizes the total task offloading delay subject to the bandwidth and computation resources constraints in VEC system. Considering the formulated problem is a mixed-integer nonlinear programming (MINLP) problem, the original problem is reformulated as a convex problem. Due to the high complexity of the problem, we decompose it into a distributed manner. By utilizing the alternating direction method of multipliers (ADMM), a joint RSUs selection and resource allocation (JRSRA) algorithm is proposed with low complexity. Simulation results shows that the proposed JRSRA algorithm can reduce the total task offloading delay compared with other benchmark methods.