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Graph-Structured Deep Learning Framework for Multi-task Contention Identification with High-dimensional Metrics
Xiao Yang, Yinan Ni, Yuqi Tang, Zhimin Qiu, Chen Wang, Tingzhou Yuan
TL;DR
High-dimensional, cross-resource contention is difficult to classify because system metrics exhibit complex dependencies and diverse task-specific patterns. The paper combines transformed representations, graph modeling, task-specific prediction, adaptive weighting, and unified inference, reporting improved classification performance and stable behavior across sensitivity analyses. It concludes that structured multi-task representations provide a technical basis for contention recognition and performance management in complex systems.
Problem
High-dimensional system environments contain complex cross-dimensional dependencies and diverse contention patterns that require unified multi-task classification.
Method
The framework combines high-dimensional representation transformation, graph-based dependency modeling, task-specific classification, adaptive task weighting, and unified inference.
Results
The proposed multi-task graph method improves contention classification performance, while moderate batch sizes and balanced training-set scales support stronger results.
Takeaways & Limitations
Structured multi-task modeling provides a technical basis for finer-grained contention recognition and applications including scheduling, resource management, and performance governance.
Abstract
from arXiv · showhide
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.
I. INTRODUCTION
High-dimensional, cross-layer metrics make contention increasingly concealed, dynamic, and difficult to classify with low-dimensional correlations. Accurate contention identification is presented as a foundation for intelligent operations and proactive resource management.
- Modern systems produce contention through interactions among multi-source metrics, heterogeneous task behaviors, and environmental uncertainty.
- Monitoring spans resource, application, and platform indicators, whose temporal dependencies support chained blocking, disturbance amplification, and repeated competition.
- Predictive scheduling, autoscaling, root-cause localization, and adaptive policy tuning require earlier detection of contention types and potential triggers.
- Accurate classification enables control loops for resource management, scheduling, throttling, and priority adjustment.
- Contention classification can reveal bottleneck structures, system pressure patterns, and cross-resource coupling while supporting capacity planning, fault prediction, and strategy optimization.
II. RELATED WORK
Related work combines temporal and structural metric modeling with reinforcement learning, transfer learning, meta-learning, and efficient adaptation. These methodological strands motivate the present integration of structured representations, graph dependencies, and adaptive multi-task learning.
- Deep learning research emphasizes temporal dependencies and multi-scale structures for robust high-dimensional metric analysis.
- Reinforcement learning and adaptive scheduling address dynamic resource-intensive environments through adaptive policy learning and multi-task optimization.
- Meta-learning and parameter-efficient fine-tuning support adaptive feature extraction, rapid task adjustment, and scalable prediction under uncertain conditions.
- The present work integrates structured representation transformation, graph-based dependency modeling, and adaptive multi-task learning for high-dimensional contention classification.
III. PROPOSED FRAMEWORK
The framework transforms high-dimensional metric sequences into structured shared representations, models cross-metric dependencies with graphs, and decouples contention types through task-specific prediction heads. Adaptive weighting and normalized inference combine these outputs into robust fine-grained predictions.
- The method extracts stable contention structure features from high-dimensional indicators and classifies contention types with a multi-task modeling mechanism.
- Representation learning: A trainable nonlinear transformation maps metric sequences into a contention-based representation space while reducing irrelevant disturbances and preserving dynamic characteristics.
- Task decoupling: Dedicated prediction heads learn differentiated features for CPU, I/O, memory, network, and hybrid contention types.
- Inference and optimization: Adaptive task weighting balances learning across prediction objectives, while normalized confidence scores make outputs comparable in the unified decision space.
- Graph modeling: Graph vertices represent metric dimensions and edges represent potential resource competition or dependency relationships, enabling structural modeling of cross-metric interactions.
- Graph modeling: Graph structure transformation captures cascading effects and chained contention across resources, improving identification of complex contention patterns.
- Inference and optimization: The complete method chain combines representation transformation, structural modeling, multi-task discrimination, dynamic weighting, and unified inference for robustness to metric noise and dynamic perturbations.
A. Dataset
The study uses Alibaba Cluster Trace 2018, a public, real-production trace with long, high-frequency, multi-level monitoring data. Its heterogeneous and cross-correlated metrics support modeling and evaluation of complex multi-task contention.
- Alibaba Cluster Trace 2018 provides real operational records from a large-scale distributed cluster containing mixed online and offline tasks.
- The dataset includes machine-, container-, and task-level metrics such as CPU, memory, disk, network, and scheduling signals.
- High recording frequency and long data spans capture dynamic resource contention, load changes, and resource over- and under-allocation in production environments.
- Its high-dimensional, multi-source, heterogeneous, dynamic, and strongly cross-correlated properties make it suitable for contention mining and multi-task classification.
B. Experimental Results
Comparative experiments show that the proposed multi-task graph method performs best across evaluation metrics, while sensitivity analyses identify moderate batch sizes and intermediate training-set scales as favorable settings.
- Comparative results: The proposed multi-task graph method achieves the best results across all metrics, including F1, among the compared models.It combines shared structural representations with task-specific prediction heads.
- Comparative results: GNNs improve performance by modeling cross-metric propagation, while GAT further improves precision and recall through attention-based dependency weighting.MLP and XGBoost remain limited in capturing nonlinear, dynamic, or graph-structured dependencies.
- Batch-size sensitivity: Moderate batch sizes of 32–64 provide more stable gradients and consistent improvements across accuracy, recall, precision, and F1.Small batches capture fine-grained fluctuations but have higher gradient variance.
- Batch-size sensitivity: Batch size 128 degrades performance because overly large batches oversmooth gradients and reduce sensitivity to local structural changes and transient contention bursts.This weakens discrimination of subtle inter-task differences.
- Training-set sensitivity: Training-set size has a non-monotonic effect: performance improves to a moderate peak before slightly declining with excessively large datasets.Small datasets underrepresent complex contention behaviors, whereas very large datasets can introduce low-contribution samples and oversmooth fine-grained features.
V. CONCLUSION
The conclusion presents a unified framework for high-dimensional multi-task contention classification that combines shared structural modeling with task-specific differences. It reports stable recognition under complex system conditions and identifies applications in resource management, while proposing broader dynamic-environment evaluation as future work.
- Framework and findings: The framework combines high-dimensional representation transformation, graph-based modeling, and task-decoupled classification to model shared structures and task-specific differences.It is designed for complex metric correlations, diverse resource competition, and unstable system dynamics.
- Framework and findings: The results indicate strong cross-dimensional dependencies and structural coupling in contention patterns, which single representations or single-task models cannot fully capture.The framework is presented as advancing system behavior modeling from a multitask perspective.
- Applications: The method provides a transferable technical basis for cloud scheduling, distributed resource management, service quality assurance, and performance governance in multi-tenant systems.The conclusion also connects structured contention classification with bottleneck identification and automated scheduling or optimization strategies.
- Future work: Future work should evaluate generalization in highly dynamic, hybrid-cloud, and higher-dimensional metric settings.The paper also proposes online learning, reinforcement-based scheduling, and adaptive optimization for real-time control loops.