By Mobile Edge Computing(MEC),computation-intensive tasks are off-loaded from mobile devices to cloud servers,and thus the energy consumption of mobile devices can be notably reduced.In this paper,we study task offloading in multi-user MEC systems with heterogeneous clouds,including edge clouds and remote clouds.Tasks are for-warded from mobile devices to edge clouds via wireless channels,and they can be further forwarded to remote clouds via the Intet.Our objective is to minimize the total energy consumption of multiple mobile devices,sub-ject to bounded-delay requirements of tasks.Based on dynamic programming,we propose an algorithm that minimizes the energy con-sumption,by jointly allocating bandwidth and computational resources to mobile devices.The algorithm is of pseudo-polynomial com-plexity.To further reduce the complexity,we propose an approximation algorithm with energy discretization,and its total energy con-sumption is proved to be within a bounded gap from the optimum.Simulation results show that,nearly 82.7％energy of mobile devices can be saved by task offloading compared with mobile device execution.