CloudRanger: Root Cause Identification for Cloud Native Systems

Ping Wang, Jingmin Xu, Meng Ma, Weilan Lin, Disheng Pan, Y. Wang, Pengfei Chen
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引用次数: 71

Abstract

As more and more systems are migrating to cloud environment, the cloud native system becomes a trend. This paper presents the challenges and implications when diagnosing root causes for cloud native systems by analyzing some real incidents occurred in IBM Bluemix (a large commercial cloud). To tackle these challenges, we propose CloudRanger, a novel system dedicated for cloud native systems. To make our system more general, we propose a dynamic causal relationship analysis approach to construct impact graphs amongst applications without given the topology. A heuristic investigation algorithm based on second-order random walk is proposed to identify the culprit services which are responsible for cloud incidents. Experimental results in both simulation environment and IBM Bluemix platform show that CloudRanger outperforms some state-of-the-art approaches with a 10% improvement in accuracy. It offers a fast identification of culprit services when an anomaly occurs. Moreover, this system can be deployed rapidly and easily in multiple kinds of cloud native systems without any predefined knowledge.
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CloudRanger:云原生系统的根本原因识别
随着越来越多的系统向云环境迁移,云原生系统成为一种趋势。本文通过分析IBM Bluemix(一个大型商业云)中发生的一些真实事件,介绍了在诊断云原生系统的根本原因时所面临的挑战和影响。为了应对这些挑战,我们提出了CloudRanger,一个专门用于云原生系统的新系统。为了使我们的系统更具通用性,我们提出了一种动态因果关系分析方法,在不给定拓扑的情况下构建应用程序之间的影响图。提出了一种基于二阶随机漫步的启发式调查算法,用于识别导致云事件的罪魁祸首服务。在模拟环境和IBM Bluemix平台上的实验结果表明,CloudRanger的准确率比一些最先进的方法提高了10%。当异常发生时,它提供了对罪魁祸首服务的快速识别。此外,该系统可以快速、轻松地部署在多种云原生系统中,无需任何预先定义的知识。
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