A Data Set for User Request Trace-Oriented Monitoring and its Applications
A Data Set for User Request Trace-Oriented Monitoring and its Applications
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DOI：
10.1109/tsc.2015.2491286
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发表时间：
2018-07
影响因子：
8.1
通讯作者：
Jingwen Zhou;Zhenbang Chen;Ji Wang;Zibin Zheng;Michael R. Lyu
中科院分区：
文献类型：
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作者：
Jingwen Zhou;Zhenbang Chen;Ji Wang;Zibin Zheng;Michael R. Lyu
面向用户请求跟踪的监控是提高云服务可靠性的有效方法。然而，在实践中获取有用的痕迹存在一些困难，这阻碍了面向痕迹的监测研究的发展。在本文中，我们发布了一个细粒度的以用户请求为中心的开放跟踪数据集，称为TraceBch，它是从一个部署在真实环境中的真实云存储服务中收集的。在收集时，我们考虑了不同的场景，涉及多个集群规模、不同类型的用户请求、不同的工作负载速度、多种类型的注入故障等。为了验证可用性和真实性，我们在异常检测、性能问题诊断和时间不变量挖掘等几个面向轨迹的监控主题中使用了TraceBitch。结果表明，TraceBENCH很好地支持了这些研究课题。此外，我们还基于TraceBch进行了广泛的数据分析，验证了数据集的高质量。
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.