A Data Set for User Request Trace-Oriented Monitoring and its Applications

User request trace-oriented monitoring is an effective method to improve the reliability of cloud services. However, there are some difficulties in getting useful traces in practice, which hinder the development of trace-oriented monitoring research. In this paper, we release a fine-grained user request-centric open trace data set, called TraceBench, which is collected in a real-world cloud storage service deployed in a real environment. When collecting, we consider different scenarios, involving multiple scales of clusters, different kinds of user requests, various speeds of workloads, many types of injected faults, etc. To validate the usability and authenticity, we have employed TraceBench in several trace-oriented monitoring topics, such as anomaly detection, performance problem diagnosis, and temporal invariant mining. The results show that TraceBench well supports these research topics. In addition, we have also carried out an extensive data analysis based on TraceBench, which validates the high quality of the data set.