About the Influence of Workflow Topology on Task Intensity Prediction through Graph Learning
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 influen...
| Authors | Max Otto, Haci Ismail Aslan, Joel Witzke et al. |
|---|---|
| Primary category | cs.DC |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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