Service Deployment and Task Offloading Algorithm for UAV-Parking-Vehicle-Assisted Mobile Edge Computing

In this paper, we study a UAV-Parking-Vehicles assisted Mobile Edge Computing (MEC) network for providing enhanced edge computing services, where a UAV serves to relay ground users’ tasks to parking vehicles having idle computing resources in the vicinity for processing.We formulate the problem of average task delay minimization in this case as a long-term discrete mixed integer programming problem. We transform this problem into two subproblems: Service deployment optimization problem at large time scale and task offloading optimization problem at small time scale. For the former, we define a system utility for measuring the effect of a service deployment profile at parking vehicles, formulate the system utility minimization problem for optimizing the service deployment at vehicles, and propose a Computing resource allocation and Genetic Algorithm based Service deployment Algorithm (CGSA) for obtaining optimized service deployment profiles at parking vehicles on a per cycle basis. For the latter, we propose a Bandwidth allocation, Task offloading, and Transmission scheduling Algorithm (BTTA) for determining optimized task offloading profiles for all users on a per time slot basis. Extensive simulation results show the high performance of our proposed algorithms compared with baseline algorithms.