Optimizing Task Execution for Mobile Edge Computing

Computation-intensive applications can be enabled by mobile edge computing (MEC) in 5G networks because MEC carries cloud computing almost near to smart devices. In this paper, we study a multi-user MEC system, where several smart devices (SDs) can fulfill computation offloading over wireless channels to a MEC server. we study the minimization of a total sum cost which is energy consumption and time delay for all the smart devices (where smart devices can choose one out of three scenarios to execute the task, i.e., full local computing scenario, full offloading execution scenario, and partial offloading execution scenario) as our objective function optimization. We mutually optimize task partition, offloading decision and computation resource sharing to reduce the total cost of the MEC system. We used an extensive search method and Lagrange method to solve these problems. Statistical results prove the effectiveness of our proposed scheme.