## 6 Simulation Results and Analysis
However, spikes are observed at certain time points, indicating sudden increases or decreases in rate. This is due to the protocol acceleration of actual switch hardware or differences in switch output queue management strategies, leading to more complex characteristics of actual awareness data and consequently affecting the corresponding prediction performance. From Figure 5c, the prediction results based on actual measurement data have a large error in the 55 th time slot. The relative error of the prediction method is 21.6%, while the relative error of the compensation method is 9.3%, indicating a significant improvement in data accuracy. From Figure 5d, it can be seen that for the time period of 60 to 65, when there is significant fluctuation in the data, the predicted results for the resource situation at the node are opposite to the actual data. However, the compensation method, by comprehensively considering the perception latency data and the predicted results, can not only reduce relative errors but also retain the trend of resource data changes, further improving the reliability of the data and providing support for subsequent resource allocation. Figure 6 presents a comparison of the SRCR for various datasets following network resource allocation utilizing the accelerated ADMM algorithm. It can be seen from the figure that when the number of service requests is less than 80, the SRCR is 100% in all data scenarios; that is, the service resource requirements in the network can be met at this time. The resource allocation approach, which relies on awareness data, suffers from the inherent latency, resulting in outdated resource status information for certain nodes and consequently rendering some services unable to fulfill their resource coverage needs. Based on the predicted results, the SRCR effect is weaker than that of the compensation data. Due to significant fluctuations in prediction accuracy, the corresponding SRCR also experiences fluctuations, resulting in poor stability of allocation performance. The SRCR based on compensation data aligns closely with the actual onboard data, accurately reflecting node status and capacity and demonstrating an effective SRCR. When the number of service requests reaches 200, the SRCR based on compensation data deviates from the actual onboard data by just 10.2%, representing a 15.8% improvement over the predicted data and a 62% enhancement compared to the awareness data with latency. Consequently,  
Figure 5: The comparison of different methods under high-bandwidth service scenario. (**a**) TPR data of non-service; (**b**) RPR data of non-service; (**c**) TPR data of high-bandwidth service; (**d**) RPR data of high-bandwidth service. the resource allocation method using compensation data can significantly enhance the efficiency of network resource utilization. Figure 7 presents a comparison of the SRCR among the proposed NRA-PPJC algorithm and two other algorithms in various service scenarios. The HEAA algorithm [27] is a historical experience average method which uses each node to allocate corresponding resources for services on an average basis according to historical experience. PBRA is a priority-based network resource allocation method [28] which gives precedence to the resource requirements of high-priority services. It can be seen from the figure that the SRCR of the NRA-PPJC method proposed in this paper is higher than that of the HEAA algorithm because HEAA does not take into account the differences in resource requirements between services, and the average division of resources for each service leads to wasted resources when the number of service requests increases. The PBRA algorithm allocates resources for high-priority services, so when the number of service requests is small, its SRCR is higher than that of the NRA-PPJC algorithm. As the number of service requests grows, despite some nodes having surplus resources, they can no longer fulfill the requirements of additional service demands.