嵌入式设备中复合型任务最优调度约束模型仿真

Simulation of Complex Task Optimal Scheduling Constraint Model in Embedded Equipment

在ARM和Linux等嵌入式操作系统设计和应用中,为了优化进程管理和内存管理,提高嵌入式设备的运行效率,需要对复合型任务进行最优调度策略规划。提出一种改进的嵌入式设备中复合型任务最优调度约束模型,设计粒子分集聚敛算法实现对嵌入式设备中复合型任务最优调度,和实现对任务调度约束模型的改进,通过进程管理子系统,完成进程的创建、中止、进程间的通信及任务调度,进行特征分解,得到调度迭代方程。采用模糊聚类策略对任务调度管理的数据信息进行约简,可以实现最小的运算量。实验表明,通过该最优调度约束模型,调度算法对整个调度过程的时间开销影响不大,由此提高了调度算法的活性能力,总执行时间节省10%左右,可以使CPU利用率达到100%,展示了其优越性。

The design and application of ARM and Linux embedded operating system, in order to optimize the process management and memory management, improve the operation efficiency of embedded devices, the need for optimal scheduling strategy planning of the composite task. A complex embedded equipment improvement of optimal scheduling task constraint model is proposed, design of particle diversity convergent algorithm for complex tasks in embedded equipment opti- mal scheduling, design of particle diversity convergent algorithm for complex tasks in embedded equipment optimal sched- uling, improved by adopting the particle diversity convergent algorithm implementation constraints on task scheduling mod- el, through the process of management subsystem, communication and task completion of the process of the creation, suspension, the inter process scheduling, the scheduling feature decomposition, iterative equations are reduced using data information. The fuzzy clustering method for task scheduling management, can achieve a minimum amount of. Experiments show that, through the optimal scheduling constraint model, scheduling algorithms for the scheduling process time overhead is not affected, thereby improving the activity scheduling algorithm, the total execution time savings of about 10%, it can make the CPU utilization rate reached 100%, showing its superiority.