Improving Pattern Recognition of Scheduling Anomalies through Structure-Aware and Semantically-Enhanced Graphs

2025-12-21

AI Summary

To address low anomaly detection accuracy and weak representational capacity in complex system scheduling—particularly under concurrent multitasking, resource contention, and phase transitions—this paper proposes a structure-guided dynamic scheduling graph modeling framework. We introduce a novel evolutionary graph construction mechanism grounded in task phases, resource states, and scheduling paths, and design a multi-scale semantic aggregation module to jointly capture local neighborhood structures and global topological consistency. The method achieves joint structural awareness and semantic coherence. Evaluated on a realistic multi-perturbation dataset, it significantly outperforms baselines: sensitivity to structural shifts, abrupt resource changes, and task delays is markedly improved; enhanced anomaly separability and pattern discriminability are empirically validated through visualization.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Models for Web evolution

Abstract

This paper proposes a structure-aware driven scheduling graph modeling method to improve the accuracy and representation capability of anomaly identification in scheduling behaviors of complex systems. The method first designs a structure-guided scheduling graph construction mechanism that integrates task execution stages, resource node states, and scheduling path information to build dynamically evolving scheduling behavior graphs, enhancing the model's ability to capture global scheduling relationships. On this basis, a multi-scale graph semantic aggregation module is introduced to achieve semantic consistency modeling of scheduling features through local adjacency semantic integration and global topology alignment, thereby strengthening the model's capability to capture abnormal features in complex scenarios such as multi-task concurrency, resource competition, and stage transitions. Experiments are conducted on a real scheduling dataset with multiple scheduling disturbance paths set to simulate different types of anomalies, including structural shifts, resource changes, and task delays. The proposed model demonstrates significant performance advantages across multiple metrics, showing a sensitive response to structural disturbances and semantic shifts. Further visualization analysis reveals that, under the combined effect of structure guidance and semantic aggregation, the scheduling behavior graph exhibits stronger anomaly separability and pattern representation, validating the effectiveness and adaptability of the method in scheduling anomaly detection tasks.

Problem

• Improves anomaly detection in complex system scheduling behaviors
• Enhances pattern recognition for multi-task concurrency and resource competition
• Addresses structural shifts, resource changes, and task delays in scheduling anomalies

Innovation

• Structure-aware scheduling graph modeling for anomaly detection
• Multi-scale semantic aggregation for feature consistency
• Dynamic graph construction capturing execution stages and resource states