Research on Service Scheduling Mechanism Based on Situation Awareness in the Power Internet of Things

In the Internet of Things (IoT) management platform, downstream commands are processed through different microservices and eventually reach the devices. However, there is an imbalance in the available computing resources between processing nodes in the microservices, resulting in uneven load distribution and thus affecting the efficiency of issuing downstream commands. In order to address this issue, a comprehensive study was conducted on the process of issuing downstream commands in the IoT management platform. The study analyzed the causes of the problem and identified the bottlenecks. A time-series-based service computing resource prediction method was proposed to predict the computing resources of the processing nodes. Based on the prediction results, a load balancing job scheduling algorithm, which is based on the prediction of service computing resources, was proposed to improve the efficiency of issuing downstream commands. Experimental results demonstrate that the proposed service scheduling mechanism based on situational awareness in the power IoT can effectively enhance the execution efficiency and success rate of downstream commands while maximizing the utilization of computing resources.