多设备间任务依赖的最佳卸载决策和资源分配

考虑了多个设备的移动边缘计算(mobile edge computing, MEC)与端对端(device-to-device, D2D)技术协作网络, 其中多个无线设备的最终输出作为另一个设备上某个子任务的输入. 为了最小化无线设备的能耗和任务完成时间的加权和, 研究了最优的资源分配(卸载发射功率和本地CPU频率)和任务卸载决策问题. 首先固定卸载决策, 推导出卸载发射功率和本地CPU频率的闭合表达式, 运用凸优化方法求出该问题的解. 然后基于一次爬升策略提出了一种低复杂度线性搜索算法, 该算法可以在线性时间内获得最佳卸载决策. 数值结果表明, 该策略的性能明显优于其他有代表性的基准测试.

Optimal Offloading Decision and Resource Allocation for Task Dependencies among Multiple Devices

The collaboration network of mobile edge computing (MEC) and device-to-device (D2D) technology takes into consideration multiple devices, where the final output of multiple wireless devices is used as the input of a subtask on another device. The optimal resource allocation (offloading transmit power and local CPU frequency) and task offloading decisions are studied to minimize the weighted sum of the energy consumption of wireless devices and the task completion time. First, given an offloading decision, the closed expression of offloading transmit power and local CPU frequency are derived, and the convex optimization method is used to find the solution to the problem. Then, on the basis of the one-climb policy, a low-complexity linear search algorithm is proposed, which can obtain the best offloading decision in linear time. Numerical results show that the performance of this strategy is significantly better than that of other representative benchmark tests.