AID：大规模云系统中依赖性聚集强度的有效预测
AID: Efficient Prediction of Aggregated Intensity of Dependency in Large-scale Cloud Systems
摘要：
服务可靠性是云提供商必须应对的关键挑战之一。在云系统中，计划外的服务故障可能会对其依赖的服务造成严重的级联影响，从而降低客户满意度。准确有效地预测级联影响对于云系统的运行和维护至关重要。现有的方法通过分布式跟踪来识别一个服务是否依赖于另一个服务，但是没有先前的工作集中在区分云服务之间的依赖性的程度上。在本文中，我们调查了两个云提供商的中断和故障诊断程序，以激励依赖强度的定义。我们将两个服务之间的依赖强度定义为被调用方服务的状态对调用方服务的影响程度。然后，我们提出了AID，第一种方法来预测云服务之间的依赖关系的强度。AID首先从跨度生成一组候选依赖对。然后，AID用从跨度聚合的多变量时间序列表示每个云服务的状态。利用服务的表示，AID计算每个候选对的调用者和被调用者的状态之间的相似度。最后，AID聚合相似性以产生作为依赖强度的统一值。我们根据从开源微服务基准测试和生产中的云系统收集的数据来评估AID。实验结果表明，AID能够有效、准确地预测依赖强度。我们进一步证明了我们的方法在大规模商业云系统中的实用性。
英文摘要：
Service reliability is one of the key challenges that cloud providers have to deal with. In cloud systems, unplanned service failures may cause severe cascading impacts on their dependent services, deteriorating customer satisfaction. Predicting the cascading impacts accurately and efficiently is critical to the operation and maintenance of cloud systems. Existing approaches identify whether one service depends on another via distributed tracing but no prior work focused on discriminating to what extent the dependency between cloud services is. In this paper, we survey the outages and the procedure for failure diagnosis in two cloud providers to motivate the definition of the intensity of dependency. We define the intensity of dependency between two services as how much the status of the callee service influences the caller service. Then we propose AID, the first approach to predict the intensity of dependencies between cloud services. AID first generates a set of candidate dependency pairs from the spans. AID then represents the status of each cloud service with a multivariate time series aggregated from the spans. With the representation of services, AID calculates the similarities between the statuses of the caller and the callee of each candidate pair. Finally, AID aggregates the similarities to produce a unified value as the intensity of the dependency. We evaluate AID on the data collected from an open-source microservice benchmark and a cloud system in production. The experimental results show that AID can efficiently and accurately predict the intensity of dependencies. We further demonstrate the usefulness of our method in a large-scale commercial cloud system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
