Task Scheduling Algorithm of Cyber-Physical System Base on Complex Industry and Big Data

In order to address the problem of unbalanced load of cyber-physical systems caused by high data concurrency, heterogeneous resources, and complex task nature in complex industrial and big data environments, this paper proposes a dynamic feedback task scheduling algorithm based on queuing theory (Queuing Theory Task Scheduling). The algorithm firstly uses the cluster monitoring system to collect data on each server and waiting queue in the cluster. Then, the monitoring system calculates various performance indicators of the system based on the collected data to judge the status of the servers. Finally, the corresponding scheduling strategy will be executed. To verify the effectiveness of queuing theory task scheduling (QTTS), this paper uses SimEvents toolkit to build an experimental environment and conduct simulation experiments about the total time to complete the tasks and average waiting time of the tasks. The experimental results show that compared with the traditional scheduling algorithms RR and Min-Min, QTTS can effectively reduce the total time to complete the tasks and average waiting time of the tasks.