云控制系统并行任务分配优化算法与并联控制

利用Petri网模拟云控制系统的并行处理过程，引入并行处理系统的时钟周期、吞吐率和任务完成时间性能指标，运用极大-加代数方法分析和优化云控制系统并行处理性能.采用子过程细分的优化方式，通过求解一类最优控制问题，设计并行任务分配优化方案，以保证任务完成时间最短，并给出计算最短任务完成时间的有效算法.同时，采用重复设置多套瓶颈段并联的方式提高并行处理能力，并运用Petri网实现瓶颈子过程的并联控制，且给出并联控制在协同云控制系统中的一个应用.

Parallel Task Assignment Optimization Algorithm and Parallel Control for Cloud Control Systems

We use Petri nets to simulate the parallel processing arising in cloud control systems. We introduce performance indexes called clock period, through-put rate and task completion time, and use the max-plus algebra to analyze and optimize the parallel processing performance of cloud control systems. By using the method of segmenting sub-processes and solving the optimal control problem, we design the optimization scheme for parallel task assignment to minimize the completion time, and develop an effective algorithm to compute such a minimum time. Computer performances can be improved through parallel connection of bottle-neck roads. We use the Petri nets to realize the parallel control of bottle-neck sub-processes and present an application of the parallel control in cooperative cloud control systems.