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

LWS: A Framework for Log-based Workload Simulation in Session-based SUT