发表机构
Technical University of Berlin（柏林工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过图学习系统评估工作流DAG拓扑对任务资源强度预测的影响，证明拓扑是关键特征，图原生模型结合手工拓扑特征可显著提升预测准确性。
AI 中文摘要
在云基础设施上为大规模工作流提供高效的资源供给是一个关键的性能工程挑战。这些工作流通常被构建为有向无环图（DAG），其中资源供给不足可能导致严重的瓶颈，而过度供给则会造成不必要的成本。准确的任务级资源强度（例如CPU负载和内存使用）预测对于缓解这些问题至关重要。虽然任务级特征常用于预测，但工作流整体拓扑结构对性能的影响往往被忽视或仅作假设。我们工作的核心问题是：DAG拓扑的哪些部分在多大程度上影响任务级资源强度，以及建模这种影响的最有效方式是什么？本文提出了一个全面的基准测试，以系统地量化图拓扑对任务强度预测的影响。我们评估并比较了一系列建模方法。我们的研究结果表明，拓扑是准确预测的关键特征。即使通过简单的手工特征纳入重要拓扑信息的模型，也显著优于基线模型。我们展示了图原生模型提供了最高的准确性，在CPU和内存预测方面均实现了较低的平均绝对误差，并且这些模型仍可与它们未学习的简单拓扑特征相结合，以获得更好的性能。
英文摘要
Efficient resource provisioning for large-scale workflows on cloud infrastructures is a critical performance engineering challenge. These workflows are often structured as directed acyclic graphs (DAGs), where under-provisioning can cause critical bottlenecks and over-provisioning leads to unnecessary costs. Accurate, task-level prediction of resource intensity (e.g., CPU load and memory usage) is essential for mitigating these issues. While task-level features are commonly used for prediction, the performance impact of the workflow's overall topological structure is often overlooked or assumed. The central question of our work is: To what extent does what part of the DAG topology influence task-level resource intensity, and what is the most effective way to model this influence? This paper presents a comprehensive benchmark to systematically quantify the impact of graph topology on task intensity prediction. We evaluate and compare a spectrum of modeling approaches. Our findings demonstrate that topology is a critical feature for accurate prediction. Models incorporating important topological information, even through simple handcrafted features, significantly outperform baseline models. We show that graph-native models provide the highest accuracy, achieving low mean absolute errors for both CPU and memory predictions, and can still be combined with simple topological features that they do not learn for better performance.
CommentsPublished at the IEEE 26th International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)
Journal refOtto, Max, et al. "About the Influence of Workflow Topology on Task Intensity Prediction Through Graph Learning." 2026 IEEE 26th International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW). IEEE, 2026
DOI：10.1109/CCGridW69005.2026.00044
