The development of emerging information technologies, such as the Internet of Things (IoT), edge computing, and blockchain, has triggered a significant increase in IoT application services and data volume. Ensuring satisfactory service quality for diverse IoT application services based on limited network resources has become an urgent issue. Generalized processor sharing (GPS), functioning as a central resource scheduling mechanism guiding differentiated services, stands as a key technology for implementing on-demand resource allocation. The performance prediction of GPS is a crucial step that aims to capture the actual allocated resources using various queue metrics. Some methods (mainly analytical methods) have attempted to establish upper and lower bounds or approximate solutions. Recently, artificial intelligence (AI) methods, such as deep learning, have been designed to assess performance under self-similar traffic. However, the proposed methods in the literature have been developed for specific traffic scenarios with predefined constraints, thus limiting their real-world applicability. Furthermore, the absence of a benchmark in the literature leads to an unfair performance prediction comparison. To address the drawbacks in the literature, an AI-enabled performance benchmark with comprehensive traffic-oriented experiments showcasing the performance of existing methods is presented. Specifically, three types of methods are employed: traditional approximate analytical methods, traditional machine learning-based methods, and deep learning-based methods. Following that, various traffic flows with different settings are collected, and intricate experimental analyses at both the feature and method levels under different traffic conditions are conducted. Finally, insights from the experimental analysis that may be beneficial for the future performance prediction of GPS are derived.

Generalized Processor Sharing (GPS) under self-similar traffic is widely used for guiding resource allocation in modern communication networks where performance evaluation, i.e., average waiting queue length and average waiting queue delay, is the key step. Due to its high complexity and unknown inherent relations, theoretically attaining the accurate performance of each queue remains unsolved. Simulation can obtain accurate performance but the time cost is unaffordable. In this paper, we address the performance evaluation for multi-queue GPS under self-similar traffic problem and propose a knowledge-driven dual-task learning approach, namely DualTask_GPS. Firstly, DualTask_GPS utilizes attention mechanisms to model the inherent relations in GPS based on augmented queue-theory-based knowledge-driven features and a gate-based server-queue module to adaptively fuse the learned information of the server and queues to obtain the intermediate capacity corresponding to each queue for the final performance prediction. To capture more reasonable representations, we jointly learn to predict the average waiting queue length and average waiting queue delay of a GPS system with posterior constraints by prior knowledge. To evaluate the performance of the DualTask_GPS model, we conduct experiments on the three widely acknowledged GPS settings. The results show that our proposed approach is effective regarding various metrics on both tasks.

Accurate identification of surface cracks is of great significance for the site selection of transmission towers, as it directly affects the safety and stability of power grid construction. Traditional manual inspection methods are labor-intensive and inefficient, making them inadequate for large-scale and high-precision applications. Consequently, intelligent crack recognition technologies are receiving increasing attention and adoption. This study proposes a novel surface crack identification method that integrates multiple neural networks, aiming to overcome the limitations of traditional crack identification approaches, such as low accuracy and poor generalization. The proposed framework incorporates a convolutional neural network (CNN)-based data filtering module and a crack segmentation module combining UNet and YOLOv8 architectures. Together, these components form a robust and accurate end-to-end crack identification system, which is further applied to the evaluation of rock fragmentation. To verify the effectiveness and accuracy of the proposed method, experiments were conducted on a rock crack dataset and compared against several existing approaches. The results demonstrate that the proposed method achieves superior performance in crack detection accuracy. Moreover, tests on various scenario datasets also yielded promising results, indicating the strong generalization ability and adaptability of the method.