This study addresses the challenge of accurately identifying multi-task contention types in high-dimensional system environments and proposes a unified contention classification framework that integrates representation transformation, structural modeling, and a task decoupling mechanism. The method first constructs system state representations from high-dimensional metric sequences, applies nonlinear transformations to extract cross-dimensional dynamic features, and integrates multiple source information such as resource utilization, scheduling behavior, and task load variations within a shared representation space. It then introduces a graph-based modeling mechanism to capture latent dependencies among metrics, allowing the model to learn competitive propagation patterns and structural interference across resource links. On this basis, task-specific mapping structures are designed to model the differences among contention types and enhance the classifier's ability to distinguish multiple contention patterns. To achieve stable performance, the method employs an adaptive multi-task loss weighting strategy that balances shared feature learning with task-specific feature extraction and generates final contention predictions through a standardized inference process. Experiments conducted on a public system trace dataset demonstrate advantages in accuracy, recall, precision, and F1, and sensitivity analyses on batch size, training sample scale, and metric dimensionality further confirm the model's stability and applicability. The study shows that structured representations and multi-task classification based on high-dimensional metrics can significantly improve contention pattern recognition and offer a reliable technical approach for performance management in complex computing environments.

翻译：本研究针对高维系统环境中准确识别多任务竞争类型的挑战，提出了一个融合表征变换、结构建模与任务解耦机制的统一竞争分类框架。该方法首先从高维指标序列构建系统状态表征，通过非线性变换提取跨维度动态特征，并在共享表征空间内整合资源利用率、调度行为及任务负载变化等多源信息。随后引入基于图的建模机制以捕捉指标间的潜在依赖关系，使模型能够学习资源链路间的竞争传播模式与结构干扰。在此基础上，设计任务特定映射结构以建模竞争类型间的差异，增强分类器对多种竞争模式的区分能力。为实现稳定性能，该方法采用自适应多任务损失加权策略，平衡共享特征学习与任务特定特征提取，并通过标准化推理过程生成最终竞争预测。在公开系统追踪数据集上的实验表明，该方法在准确率、召回率、精确率与F1分数上均具优势；对批处理规模、训练样本量及指标维度的敏感性分析进一步验证了模型的稳定性与适用性。研究表明，基于高维指标的结构化表征与多任务分类能显著提升竞争模式识别能力，为复杂计算环境中的性能管理提供了可靠的技术途径。