Artificial intelligence for IT Operations (AIOps) plays a critical role in operating and managing cloud-native systems and microservice-based applications but is limited by the lack of high-quality datasets with diverse scenarios. Realistic workloads are the premise and basis of generating such AIOps datasets, with the session-based workload being one of the most typical examples. Due to privacy concerns, complexity, variety, and requirements for reasonable intervention, it is difficult to copy or generate such workloads directly, showing the importance of effective and intervenable workload simulation. In this paper, we formulate the task of workload simulation and propose a framework for Log-based Workload Simulation (LWS) in session-based systems. LWS extracts the workload specification including the user behavior abstraction based on agglomerative clustering as well as relational models and the intervenable workload intensity from session logs. Then LWS combines the user behavior abstraction with the workload intensity to generate simulated workloads. The experimental evaluation is performed on an open-source cloud-native application with both well-designed and public real-world workloads, showing that the simulated workload generated by LWS is effective and intervenable, which provides the foundation of generating high-quality AIOps datasets.

翻译：面向IT运营的人工智能（AIOps）在运维管理云原生系统和微服务应用中发挥着关键作用，但受限于缺乏涵盖多样化场景的高质量数据集。真实工作负载是生成此类AIOps数据集的前提和基础，其中基于会话的工作负载是最典型的示例之一。由于隐私问题、复杂性、多样性以及需进行合理干预等因素，直接复制或生成这类工作负载存在困难，这凸显了有效且可干预的工作负载模拟的重要性。本文定义了工作负载模拟任务，并提出了一种面向基于会话系统的日志驱动工作负载模拟框架（LWS）。LWS从会话日志中提取工作负载规格说明，包括基于凝聚聚类的用户行为抽象、关系模型以及可干预的工作负载强度。随后，LWS将用户行为抽象与工作负载强度相结合，生成模拟工作负载。在开源云原生应用上使用精心设计的真实工作负载和公开的真实工作负载进行的实验评估表明，LWS生成的模拟工作负载具有有效性和可干预性，为生成高质量AIOps数据集奠定了基础。