Multiobjective Optimization-Based Task Offloading Combined With Power and Resource Allocation in Mobile Edge Computing

For mobile devices with limited computing power, it is a challenging problem to meet the demand of low-delay tasks and reduce their energy consumption and cost in mobile edge computing (MEC). In the multidevice and multiserver computing offloading model in MEC, we consider that each device has different computing power and independent tasks with different computational workload, and combine three optimization objectives of task offloading, power allocation, and resource allocation to construct a multiobjective optimization problem. To solve the abovementioned problem, we propose an efficient multiobjective evolutionary algorithm. Compared with the related algorithms, the proposed method can more effectively minimize the computational delay, device energy consumption, and cost. Simulation results show that the offloading revenue of mobile devices has been significantly optimized.