Network traffic anomaly detection mainly detects and analyzes abnormal traffic by extracting the statistical features of network traffic. It is necessary to fully understand the concept of symmetry in anomaly detection and anomaly mitigation. However, the original information on network traffic is easily lost, and the adjustment of dynamic network configuration becomes gradually complicated. To solve this problem, we designed and realized a new online anomaly detection system based on software defined networks. The system uses the convolutional neural network to directly extract the original features of the network flow for analysis, which can realize online real- time packet extraction and detection. It utilizes SDN to flexibly adapt to changes in the network, allowing for a zero-configuration anomaly detection system. The packet filter of the anomaly detection system is used to automatically implement mitigation strategies to achieve online real-time mitigation of abnormal traffic. The experimental results show that the proposed method is more accurate and can warn the network manager in time that security measures can be taken, which fully demonstrates that the method can effectively detect abnormal traffic problems and improve the security performance of edge clustering networks.

Detecting bearing defects accurately and efficiently is critical for industrial safety and efficiency. This paper introduces Bearing-DETR, a deep learning model optimised using the Real-Time Detection Transformer (RT-DETR) architecture. Enhanced with Dysample Dynamic Upsampling, Efficient Model Optimization (EMO) with Meta-Mobile Blocks (MMB), and Deformable Large Kernel Attention (D-LKA), Bearing-DETR offers significant improvements in defect detection while maintaining a lightweight framework suitable for low-resource devices. Validated on a dataset from a chemical plant, Bearing-DETR outperformed the standard RT-DETR, achieving a mean average precision (mAP) of 94.3% at IoU = 0.5 and 57.5% at IoU = 0.5–0.95. It also reduced floating-point operations (FLOPs) to 8.2 G and parameters to 3.2 M, underscoring its enhanced efficiency and reduced computational demands. These results demonstrate the potential of Bearing-DETR to transform maintenance strategies and quality control across manufacturing environments, emphasising adaptability and impact on sustainability and operational costs.

In industrial manufacturing, bearings are crucial for machinery stability and safety. Undetected wear or cracks can lead to severe operational and financial setbacks. Thus, accurately identifying bearing defects is essential for maintaining production safety and equipment reliability. This research introduces an improved bearing defect detection model, YOLOv8-LMG, which is based on the YOLOv8n framework and incorporates four innovative technologies: the VanillaNet backbone network, the Lion optimizer, the CFP-EVC module, and the Shape-IoU loss function. These enhancements significantly increase detection efficiency and accuracy. YOLOv8-LMG achieves a mAP@0.5 of 86.5% and a mAP@0.5–0.95 of 57.0% on the test dataset, surpassing the original YOLOv8n model while maintaining low computational complexity. Experimental results reveal that the YOLOv8-LMG model boosts accuracy and efficiency in bearing defect detection, showcasing its significant potential and practical value in advancing industrial inspection technologies.

The majority of large-scale data intensive applications designed by MapReduce model are deployed and executed on a large-scale distributed Hadoop system. Running such application on a large cluster requires a large amount of energy. Therefore improving energy efficiency and minimizing energy consumption when executing each MapReduce job is a critical concern for data centers. We propose a control model based on model prediction control (MPC) for improving energy efficiency of Hadoop cluster while satisfying performance goal. The controller can dynamically scale CPU frequency in facing the workloads changes. The wavelet neural network is utilized to build the prediction model, which can accurately estimate energy efficiency ratio based on the features extracting from the variation of Hadoop workloads. With the help of a wavelet neural network, the controller can guarantee the energy efficiency ratio converges to an ideal set point within a finite settling time. We perform the extensive experiments on a Hadoop cluster to compare our controller with the previous MPC controller. The results demonstrate that our controller outperforms the baseline on energy efficiency control and adapts to workloads of Hadoop cluster.

With the development of IoT and 5G technologies, edge computing has become a key driver for providing compute, network and storage services. The dramatic increase in data size and the complexity of AI computation models have put higher demands on the performance of edge computing. Rational and optimal scheduling of AI data-intensive computation tasks can greatly improve the overall performance of edge computing. To this end, a particle swarm algorithm based on objective ranking is proposed to optimize task execution time and scheduling cost by designing a task scheduling model to achieve task scheduling in an edge computing environment. It is necessary to fully understand the concept of symmetry of resource utilization and task execution cost indicators. The method utilizes nonlinear inertia weights and shrinkage factor update mechanisms to improve the optimization-seeking ability and convergence speed of the particle-to-task scheduling solution space. The task execution time and scheduling cost are greatly reduced. Simulation experiments are conducted using the Cloudsim toolkit to experimentally compare the proposed algorithm TS-MOPSO with three other particle swarm improvement algorithms, and the experimental results show that the task execution time, maximum completion time and total task scheduling cost are reduced by 31.6%, 23.1% and 16.6%, respectively. The method is suitable for handling large and complex AI data-intensive task scheduling optimization efforts.