Deep Temporal Convolutional Neural Networks with Attention Mechanisms for Resource Contention Classification in Cloud Computing

This paper addresses the challenge of efficiently identifying and classifying resource contention behaviors in cloud computing environments. It proposes a deep neural network method based on multi-scale temporal modeling and attention-based feature enhancement. The method takes time series resource monitoring data as input. It first applies a Multi-Scale Dilated Convolution (MSDC) module to extract features from resource usage patterns at different temporal resolutions. This allows the model to capture the multi-stage dynamic evolution of resource contention behaviors. An Attention-based Feature Weighting (AFW) module is then introduced. It learns attention weights along both the temporal and feature dimensions. This enables the model to emphasize key time segments and core resource metrics through saliency modeling and feature enhancement. The overall architecture supports end-to-end modeling. It can automatically learn temporal patterns of resource contention without relying on manual feature engineering. To evaluate the effectiveness of the proposed method, this study constructs a range of contention scenarios based on real-world cloud platform data. The model is assessed under different structural configurations and task conditions. The results show that the proposed model outperforms existing mainstream temporal classification models across multiple metrics, including accuracy, recall, F1-score, and AUC. It demonstrates strong feature representation and classification capabilities, especially in handling high-dimensional, multi-source, and dynamic data. The proposed approach offers practical support for resource contention detection, scheduling optimization, and operational management in cloud platforms.