具有能量收集设备的移动边缘计算资源分配研究

针对具有能量收集能力的移动边缘计算系统的计算资源分配问题，提出一种基于李雅普诺夫贪婪优化算法。构建在设备电池电量逐渐收敛下，移动设备时延与能耗联合成本的动态最小化优化问题。利用李雅普诺夫动态优化理论，将优化问题分解成每个时隙最佳本地执行、卸载执行和能量收集3个子问题，通过线性规划获得子问题最优解。通过在本地执行、卸载执行和任务丢弃之间选择执行模式，获得设备的时延与能耗联合成本最小结果。利用键值对设计贪婪策略程序，以适应多用户多服务器系统。仿真结果证实，在保证所有设备电池电量都在规定操作水平附近稳定情况下，卸载率可达99.9%以上，并能有效降低服务延时和系统能耗。

Research on Mobile Edge Computing Resource Allocation with Energy Harvesting Device

In order to solve the problem of computing resource allocation of mobile edge computing system with energy gathering ability, an algorithm based on Lyapunov greed optimization (LGO) is proposed. This paper presents a dynamic optimization problem to minimize the combined cost of time delay and energy consumption of mobile devices under the gradual convergence of equipment battery power. Using Lyapunov dynamic optimization theory, the optimization problem is decomposed into three sub-problems of optimal local execution, unloading execution and energy harvesting for each time slot, and the optimal solution of the sub-problems is obtained by linear programming. By selecting the execution mode between local execution, unload execution and task discarding, the combined cost of time delay and energy consumption of the vehicle can be minimized. The greedy policy program is designed by using key-value pairs to adapt to multi-user and multi-server systems. The simulation results show that under the condition that the battery power of all equipment is stable around the specified operating level, the unloading rate can reach more than 99.9%, and the service delay and system energy consumption can be effectively reduced.