  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.
