With the rapid growth of cloud computing, efficient operational optimization and resource scheduling of complex cloud business processes rely on real-time and accurate performance prediction. Previous research on cloud computing performance prediction focused on qualitative (heuristic rules), model-driven, or coarse-grained time-series prediction, which ignore the study of historical performance, resource allocation status and service sequence relationships of workflow services. There are even fewer studies on prediction for workflow graph data due to the lack of available public datasets. In this study, from Alibaba Cloud's Cluster-trace-v2018, we extract nearly one billion offline task instance records into a new dataset, which contains approximately one million workflows and their corresponding directed acyclic graph (DAG) matrices. We propose a novel workflow performance prediction model (DAG-Transformer) to address the aforementioned challenges. In DAG-Transformer, we design a customized position encoding matrix and an attention mask for workflows, which can make full use of workflow sequential and graph relations to improve the embedding representation and perception ability of the deep neural network. The experiments validate the necessity of integrating graph-structure information in workflow prediction. Compared with mainstream deep learning (DL) methods and several classic machine learning (ML) algorithms, the accuracy of DAG-Transformer is the highest. DAG-Transformer can achieve 85-92% CPU prediction accuracy and 94-98% memory prediction accuracy, while maintaining high efficiency and low overheads. This study establishes a new paradigm and baseline for workflow performance prediction and provides a new way for facilitating workflow scheduling.

随着云计算的快速发展，复杂云业务流程的高效运营优化和资源调度依赖于实时准确的性能预测。以往对云计算性能预测的研究主要集中在定性（启发式规则）、模型驱动或粗粒度时间序列预测上，忽略了对工作流服务的历史性能、资源分配状态和服务顺序关系的研究。由于缺乏可用的公共数据集，关于工作流图数据预测的研究更少。在这项研究中，我们从阿里云的 Cluster-trace-v2018 中提取了近十亿个离线任务实例记录到一个新的数据集中，其中包含大约一百万个工作流及其对应的有向无环图（DAG）矩阵。我们提出了一种新颖的工作流性能预测模型（DAG-Transformer）来解决上述挑战。在 DAG-Transformer 中，我们为工作流设计了定制的位置编码矩阵和注意力掩码，可以充分利用工作流序列和图关系来提高深度神经网络的嵌入表示和感知能力。实验验证了在工作流预测中集成图结构信息的必要性。与主流深度学习（DL）方法和几种经典机器学习（ML）算法相比，DAG-Transformer的准确率最高。DAG-Transformer 可以实现 85-92% 的 CPU 预测准确率和 94-98% 的内存预测准确率，同时保持高效率和低开销。