一种能量收集嵌入式系统自适应调度算法

能量收集嵌入式系统(energy harvesting embedded system,简称EHES)的任务调度算法需要考虑能量收集单元的能量输出、能量存储单元的能量水平和能量消耗单元的能耗.实时任务在满足能量约束的条件下,才可能满足时间约束.在这个背景下,传统固定优先级调度算法不再适用于EHES.提出一种基于分组的自适应任务调度算法,它能根据能量收集单元由于能量输出的不确定性而造成的非能量约束情况和能量约束情况,自适应地选择任务调度算法.在非能量约束的情况下,减少任务抢占次数,增强任务的可调度性;在能量约束情况下,减少电池模式切换次数,提高能量存储单元的平均能量水平,从而降低系统能量约束.在一个可进行大范围任务集合仿真的实验环境下对提出的算法进行验证,并将基于分组的自适应调度算法与现有的两个经典算法进行了对比.

Adaptive Scheduling Algorithm for Energy Harvesting Embedded System

The task scheduling of energy harvesting embedded systems (EHES) should take into account the energy supply of energy harvesting unit, the energy level of energy storage unit and the energy consumption of energy dissipation unit. A real-time task can meet time constraint only if its energy constraint is satisfied. Against this background, conventional fixed-priority tasks scheduling algorithms are not suitable for EHES. A group-based adaptive task scheduling algorithm is proposed in this paper. It can select suitable task scheduling algorithm adaptively according to the non-energy constraint condition and the energy constraint condition caused by the uncertain energy supply of energy harvesting unit. In the case of non-energy constraints, the algorithm can reduce the tasks preemptions and enhance the tasks schedulability. In the case of energy constraints, the algorithm can reduce the battery-mode switches and increase the average energy level of energy storage unit, thus decrease the system energy constraint. The proposed algorithm is validated with large scale simulations comparing with other two existing classical algorithms.