Latency Optimization for Mobile Edge Computing with Dynamic Energy Harvesting

In mobile edge computing, the energy harvesting enables sustainable work of battery-powered mobile devices. However, most existing works in mobile edge computing with energy harvesting do not consider the task dependency and dynamic energy harvesting. In this paper, a model by considering the task dependency and dynamic energy harvesting is proposed. The problem of task completion time minimization is formulated, which is NP-hard. A dynamic energy harvesting strategy based on the wireless power transfer is proposed to solve the problem by allocating time slots dynamically for energy harvesting of mobile devices. Besides, we propose a greedy algorithm by giving priority to offload the sub-tasks with task dependency to the place with minimum completion time. Meanwhile, a simulated annealing algorithm is customized to refine the solution generated by the proposed greedy algorithm. Numerical results show that, the proposed algorithms outperform the random algorithm in terms of task completion time. Meanwhile, the performance of proposed greedy algorithm is better than the proposed simulated annealing algorithm in terms of the running time of algorithm.