Convex Optimization on Vehicular End–Edge–Cloud Cooperative Task Offloading

MEC is an effective paradigm in supporting computation-intensive applications. In vehicular networks, the MEC server is deployed at the roadside for task offloading. However, due to the unique characteristics of vehicular networks, including high mobility of vehicles, dynamic distribution of vehicle densities, and heterogeneous capacities of MEC servers, it is still challenging to implement efficient task offloading mechanism in MEC-assisted vehicular networks. In this chapter, we investigate a novel scenario of task offloading in the MEC-assisted architecture, where task uploading among multiple vehicles, task migration among MEC/cloud servers, and task computation among MEC/cloud severs are comprehensively investigated. On this basis, we formulate the Cooperative Task Offloading (CTO) problem by modeling the procedure of task upload, migration, and computation based on queuing theory, which aims at minimizing the delay of task completion. To tackle the CTO problem, we propose a Probabilistic Computation Offloading (PCO) algorithm, which enables the MEC server to independently make scheduling based on the derived allocation probability. Specifically, the PCO transforms the objective function into augmented Lagrangian and achieves the optimal solution in an iterative way, based on a convex framework called Alternating Direction Method of Multipliers (ADMMs). Finally, we build the simulation model, and the comprehensive simulation results show the superiority of the proposed algorithm under a wide range of scenarios.